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setwd("/Users/jayshreenohar/Downloads")##Merge datasets
df_Johnson_2017#Nasdaq
#2017-2018
df_NASDAQ <- merge(Tweets_Nasdaq, NASDAQ_new, by = "date", all.x = TRUE)
write.csv(df_NASDAQ, file = "Nasdaq_2017", row.names = FALSE)
df_NASDAQ#Autodesk
#2018
df_autodesk <- merge(Autodesk_Tweets, Autodesk_SP_2018, by = "date", all.x = TRUE)
write.csv(df_autodesk, file = "autodesk_2018", row.names = FALSE)
df_autodesk#Hasbro
#2018
df_Hasbro_2018 <- merge(Hasbro_Tweets_2018, Hasbro_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Hasbro_2018, file = "Hasbro_2018", row.names = FALSE)
#2016
df_Hasbro_2016 <- merge(Hasbro_Tweets_2016, Hasbro_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Hasbro_2016, file = "Hasbro_2018", row.names = FALSE)
df_Hasbro_2016df_Hasbro_2018#Intel
#2018
df_Intel_2018 <- merge(Intel_Tweets_2018, Intel_2018_SP, by = "date", all.x = TRUE)
write.csv(df_Intel_2018, file = "Intel_2018", row.names = FALSE)
#2016
df_Intel_2016 <- merge(Intel_Tweets_2016, Intel_2016, by = "date", all.x = TRUE)
write.csv(df_Intel_2016, file = "Intel_2016", row.names = FALSE)
df_Intel_2016df_Intel_2018#Activision
#2016
df_Activision_2016 <- merge(Activision_Tweets_2016, Activision_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Activision_2016, file = "Activision_2016", row.names = FALSE)
#2018
df_Activision_2018 <- merge(Activision_Tweets_2018, Activision_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Activision_2018, file = "Activision_2018", row.names = FALSE)
df_Activision_2016df_Activision_2018#Inuit
#2016
df_Inuit_2016 <- merge(Tweets_Inuit_2016, Inuit_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Inuit_2016, file = "Intuit_2016", row.names = FALSE)
#2018
df_Inuit_2018 <- merge(Tweets_Inuit_2018, Intuit_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Inuit_2018, file = "Intuit_2018", row.names = FALSE)
df_Inuit_2016df_Inuit_2018#Allergan
#2016
df_Allergan_2016 <- merge(Allergan_Tweets_2016, Allergan_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Allergan_2016, file = "Allergan_2016", row.names = FALSE)
#2018
df_Allergan_2018 <- merge(Allergan_Tweets_2018, Allergan_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Allergan_2018, file = "Allergan_2018", row.names = FALSE)
df_Allergan_2016df_Allergan_2018#Humana
#2016
df_Humana_2016 <- merge(Humana_Tweets_2016, Humana_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Humana_2016, file = "Humana_2016", row.names = FALSE)
#2018
df_Humana_2018 <- merge(Humana_2018_Tweets, Humana_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Humana_2018, file = "Humana_2018", row.names = FALSE)
df_Humana_2016df_Humana_2018#Autodesk_Carl_Bass
#2016
df_CB_autodesk_2016 <- merge(Autodesk_CB_tweets_2016, Autodesk_CB_SP_2016, by = "date", all.x = TRUE)
write.csv(df_CB_autodesk_2016, file = "Autodesk_CB_2016", row.names = FALSE)
#2018
df_CB_autodesk_2018 <- merge(Autodesk_CB_tweets_2017, Autodesk_2017_SP_CB, by = "date", all.x = TRUE)
write.csv(df_CB_autodesk_2018, file = "Autodesk_CB_2018", row.names = FALSE)
df_CB_autodesk_2016df_CB_autodesk_2018#Equinox
#2018
df_equinox_2018 <- merge(Equinox_2018_Tweets, Equinox_2018_SP, by = "date", all.x = TRUE)
write.csv(df_equinox_2018, file = "Equinox_2018", row.names = FALSE)
df_equinox_2018#Cisco
#2016
df_cisco_2016 <- merge(Cisco_Tweets_2016, Cisco_SP_2016, by = "date", all.x = TRUE)
write.csv(df_cisco_2016, file = "Cisco_2016", row.names = FALSE)
#2018
df_cisco_2018 <- merge(Cisco_Tweets_2018, Cisco_SP_2018, by = "date", all.x = TRUE)
write.csv(df_cisco_2018, file = "Cisco_2018", row.names = FALSE)
df_cisco_2016df_cisco_2018#EBAY
#2016
df_EBAY_2016 <-merge(EBAY_Tweets_2016, EBAY_2016_SP, by = "date", all.x = TRUE)
write.csv(df_EBAY_2016, file = "EBAY_2016", row.names = FALSE)
#2018
df_EBAY_2018 <-merge(EBAY_Tweets_2018, EBAY_SP_2018, by = "date", all.x = TRUE)
write.csv(df_EBAY_2018, file = "EBAY_2018", row.names = FALSE)
df_EBAY_2016df_EBAY_2018#Davita
#2016
df_Davita_2016 <-merge(Tweets_Davita_2016, Davita_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Davita_2016, file = "Davita_2016", row.names = FALSE)
#2017
df_Davita_2017 <-merge(Davita_Inc_Tweets_2017, Davita_SP_2017, by = "date", all.x = TRUE)
write.csv(df_Davita_2017, file = "Davita_2017", row.names = FALSE)
df_Davita_2016df_Davita_2017#Illumina
#2016
df_Illumina_2016 <-merge(Illumina_Tweets_2016, Illumina_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Illumina_2016, file = "Illumina_2016", row.names = FALSE)
#2018
df_Illumina_2018 <-merge(Illumina_Tweets_2017_2018, Illumina_2017_2018_SP, by = "date", all.x = TRUE)
write.csv(df_Illumina_2018, file = "Illumina_2018", row.names = FALSE)
df_Illumina_2016df_Illumina_2018#HomeDepot
#2013-2014
df_Homedepot_2013 <-merge(Home_depot_tweets_2013,Home_Depot_SP, by = "date", all.x = TRUE)
write.csv(df_Homedepot_2013, file = "Homedepot_2013", row.names = TRUE)
df_Homedepot_2013#Southwest_Airlines
#2016
df_Southwest_2016 <-merge(Southwest_Tweets_2016,Southwest_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Southwest_2016, file = "Southwest_2016", row.names = FALSE)
#2018
df_Southwest_2018 <-merge(Southwest_Tweets_2018,Southwest_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Southwest_2018, file = "Southwest_2018", row.names = FALSE)
df_Southwest_2016df_Southwest_2018#FIS
#2016
df_FIS_2016 <-merge(FIS_Tweets_2016, FIS_2016_SP, by = "date", all.x = TRUE)
write.csv(df_FIS_2016, file = "FIS_2016", row.names = FALSE)
#2018
df_FIS_2018 <-merge(FIS_Tweets_2018, FIS_2018_SP, by = "date", all.x = TRUE)
write.csv(df_FIS_2018, file = "FIS_2018", row.names = FALSE)
df_FIS_2016df_FIS_2018#Leucadia_Nation
#2016
df_Leucadia_2016 <-merge(Leucadia_Tweets_2016, Leucadia_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Leucadia_2016, file = "Leucadia_2016", row.names = FALSE)
#2018
df_Leucadia_2018 <-merge(Leucadia_2018_Tweets, Leucadia_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Leucadia_2018, file = "Leucadia_2018", row.names = FALSE)
df_Leucadia_2016df_Leucadia_2018#Verizon
#2018
df_Verizon_2018 <- merge(Verizon_Tweets_2018, Verizon_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Verizon_2018, file = "Verizon_2018", row.names = FALSE)
df_Verizon_2018#Western Union
#2018
df_WU_2018 <- merge(Western_Union_Tweets_2018, WU_SP_2018, by = "date", all.x = TRUE)
write.csv(df_WU_2018, file = "WU_2018", row.names = FALSE)
#2016
df_WU_2016 <- merge(Western_Union_Tweets_2016, WU_SP_2016, by = "date", all.x = TRUE)
write.csv(df_WU_2016, file = "WU_2016", row.names = FALSE)
df_WU_2016df_WU_2018#Red_Hat
#2016
df_RedHat_2016 <- merge(Red_Hat_Tweets_2016, Red_Hat_SP_2016, by = "date", all.x = TRUE)
write.csv(df_RedHat_2016, file = "RedHat_2016", row.names = FALSE)
#2018
df_RedHat_2018 <- merge(Red_Hat_Tweets_2018, Red_Hat_SP_2018, by = "date", all.x = TRUE)
write.csv(df_RedHat_2018, file = "RedHat_2018", row.names = FALSE)
df_RedHat_2016df_RedHat_2018#AMZN
#2016
df_AMZN_2016 <- merge(AMZN_Tweets_2016, AMZN_SP_2016, by = "date", all.x = TRUE)
write.csv(df_AMZN_2016, file = "AMZN_2016", row.names = FALSE)
#2018
df_AMZN_2018 <- merge(AMZN_Tweets_2018, AMZN_SP_2018, by = "date", all.x = TRUE)
write.csv(df_AMZN_2018, file = "AMZN_2018", row.names = FALSE)
df_AMZN_2016df_AMZN_2018#GE
#2016
df_GE_2016 <- merge(GE_Tweets_2016, GE_2016_SP, by = "date", all.x = TRUE)
write.csv(df_GE_2016, file = "GE_2016", row.names = FALSE)
#2017
df_GE_2017 <- merge(GE_Tweets_2017, GE_2017_SP, by = "date", all.x = TRUE)
write.csv(df_GE_2017, file = "GE_2017", row.names = FALSE)
df_GE_2016df_GE_2017#Fiserv
#2016
df_Fiserv_2016 <- merge(Fiserv_Tweets_2016, Fiserv_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Fiserv_2016, file = "Fiserv_2016", row.names = FALSE)
#2018
df_Fiserv_2018 <- merge(Fiserv_Tweets_2018, Fiserv_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Fiserv_2018, file = "Fiserv_2018", row.names = FALSE)
df_Fiserv_2016df_Fiserv_2018#Waste_Management
#2018
df_WM_2018 <- merge(WM_Tweets_2018, WM_SP_2018, by = "date", all.x = TRUE)
write.csv(df_WM_2018, file = "WM_2018", row.names = FALSE)
df_WM_2018#Wills_Tower
#2016
df_Wills_2016 <- merge(Wills_Tower_Tweets_2016, Wills_Tower_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Wills_2016, file = "Wills_2016", row.names = FALSE)
#2018
df_Wills_2018 <- merge(Wills_Tower_Tweets_2018, WLTW_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Wills_2018, file = "Wills_2018", row.names = FALSE)
df_Wills_2016df_Wills_2018#Tripadvisor
#2016
df_tripadvisor_2016 <- merge(Tripadvisor_Tweets_2016, Tripadvisor_SP_2016, by = "date", all.x = TRUE)
write.csv(df_tripadvisor_2016, file = "Tripadvisor_2016", row.names = FALSE)
#2018
df_tripadvisor_2018 <- merge(Tripadvisor_Tweets_2018, Tripadvisor_SP_2018, by = "date", all.x = TRUE)
write.csv(df_tripadvisor_2018, file = "Tripadvisor_2018", row.names = FALSE)
df_tripadvisor_2016df_tripadvisor_2018#Davita_Kent
#2016
df_DavitaK_2016 <- merge(Davita_Tweets_2016K, Davita_SP_2016, by = "date", all.x = TRUE)
write.csv(df_DavitaK_2016, file = "Davita_K_2016", row.names = FALSE)
#2018
df_DavitaK_2018 <- merge(Davita_Tweets_2018K, Davita_SP_2018, by = "date", all.x = TRUE)
write.csv(df_DavitaK_2018, file = "Davita_K_2018", row.names = FALSE)
df_DavitaK_2016df_DavitaK_2018#Starbucks
df_Starbucks_2018 <- merge(Starbucks_Tweets_2018, Starbucks_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Starbucks_2018, file = "Starbucks_2018", row.names = FALSE)
df_Starbucks_2018#McCormick
#2018
df_McCormick_2018 <- merge(McCormick_Tweets_2018, McCormick_SP_2018, by = "date", all.x = TRUE)
write.csv(df_McCormick_2018, file = "McCormick_2018", row.names = FALSE)
df_McCormick_2018#IHS Markit
#2018
df_IHS_2018 <- merge(IHS_Tweets_2018, IHS_SP_2018, by = "date", all.x = TRUE)
write.csv(df_IHS_2018, file = "IHS_2018", row.names = FALSE)
df_IHS_2018#AMD
#2018
df_AMD_2018 <- merge(AMD_Tweets_2018, AMD_SP_2018, by = "date", all.x = TRUE)
write.csv(df_AMD_2018, file = "AMD_2018", row.names = FALSE)
df_AMD_2018#ResMed
#2016
df_ResMed_2016 <-merge(ResMed_Tweets_2016, ResMed_SP_2016, by = "date", all.x = TRUE)
write.csv(df_ResMed_2016, file = "ResMed_2016", row.names = FALSE)
#2018
df_ResMed_2018 <-merge(ResMed_Tweets_2018, ResMed_SP_2018, by = "date", all.x = TRUE)
write.csv(df_ResMed_2018, file = "ResMed_2018", row.names = FALSE)
df_ResMed_2016df_ResMed_2018#CA
#2016
df_CA_2016 <-merge(CA_Tweets_2016, CA_SP_2016, by = "date", all.x = TRUE)
write.csv(df_CA_2016, file = "CA_2016", row.names = FALSE)
#2018
df_CA_2018 <-merge(CA_Tweets_2018, CA_SP_2018, by = "date", all.x = TRUE)
write.csv(df_CA_2018, file = "CA_2018", row.names = FALSE)
df_CA_2016df_CA_2018#General_Motors
#2016
df_GM_2016 <-merge(GM_Tweets_2016, GM_SP_2016, by = "date", all.x = TRUE)
write.csv(df_GM_2016, file = "GM_2016", row.names = FALSE)
#2018
df_GM_2018 <-merge(GM_Tweets_2018, GM_SP_2018, by = "date", all.x = TRUE)
write.csv(df_GM_2018, file = "GM_2018", row.names = FALSE)
df_GM_2016df_GM_2018#Aetna
#2016
df_Aetna_2016 <-merge(Aetna_Tweets_2016, AET_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Aetna_2016, file = "Aetna_2016", row.names = FALSE)
#2018
df_Aetna_2018 <-merge(Aetna_Tweets_2018, Aetna_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Aetna_2018, file = "Aetna_2018", row.names = FALSE)
df_Aetna_2016df_Aetna_2018#NRG
#2016
df_NRG_2016 <-merge(NRG_Tweets_2016, NRG_SP_2016, by = "date", all.x = TRUE)
write.csv(df_NRG_2016, file = "NRG_2016", row.names = FALSE)
#2018
df_NRG_2018 <-merge(NRG_Tweets_2018, NRG_SP_2018, by = "date", all.x = TRUE)
write.csv(df_NRG_2018, file = "NRG_2018", row.names = FALSE)
df_NRG_2016df_NRG_2018#Medtronic
#2016
df_Medtronic_2016 <-merge(Medtronic_Tweets_2016, Medtronic_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Medtronic_2016, file = "Medtronic_2016", row.names = FALSE)
#2018
df_Medtronic_2018 <-merge(Medtronic_Tweets_2018, Medtronic_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Medtronic_2018, file = "Medtronic_2018", row.names = FALSE)
df_Medtronic_2016df_Medtronic_2018#Juniper
#2016
df_Juniper_2016 <-merge(Juniper_Tweets_2016, Juniper_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Juniper_2016, file = "Juniper_2016", row.names = FALSE)
#2018
df_Juniper_2018 <-merge(Juniper_Tweets_2018, Juniper_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Juniper_2018, file = "Juniper_2018", row.names = FALSE)
df_Juniper_2016df_Juniper_2018#Aetna_Ramon_2018
#2018
df_AetnaR_2018 <-merge(Aetna_Tweets_2018R, AetnaR_SP_2018, by = "date", all.x = TRUE)
write.csv(df_AetnaR_2018, file = "AetnaR_2018", row.names = FALSE)
df_AetnaR_2018#Netflix
#2016
df_Netflix_2016 <-merge(Netflix_Tweets_2016, Netflix_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Netflix_2016, file = "Netflix_2016", row.names = FALSE)
df_Netflix_2016#Disney
df_Disney_2018 <-merge(Disney_Tweets_2018, Disney_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Disney_2018, file = "Disney_2018", row.names = FALSE)
df_Disney_2018#FOX
#2015
df_Fox_2015 <-merge(Fox_Tweets_2015, Fox_SP_2015, by = "date", all.x = TRUE)
write.csv(df_Fox_2015, file = "Fox_2015", row.names = FALSE)
df_Fox_2015#Microsoft
#2016
df_Microsoft_2016 <-merge(Microsoft_Tweets_2016, Microsoft_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Microsoft_2016, file = "Microsoft_2016", row.names = FALSE)
#2018
df_Microsoft_2018 <-merge(Microsoft_Tweets_2018, Microsoft_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Microsoft_2018, file = "Microsoft_2018", row.names = FALSE)
df_Microsoft_2016df_Microsoft_2018#Juniper_Shaygan
#2014
df_Juniper_S_2014 <-merge(JuniperS_Tweets_2014, Juniper_SP_2014, by = "date", all.x = TRUE)
write.csv(df_Juniper_S_2014, file = "Juniper_S_2014", row.names = FALSE)
df_Juniper_S_2014
df_Juniper_S_2016#Synchrony Financial
#2016
df_SYFM_2016 <-merge(SYFM_Tweets_2016, SYF_SP_2016, by = "date", all.x = TRUE)
write.csv(df_SYFM_2016, file = "SYFM_2016", row.names = FALSE)
#2018
df_SYFM_2018 <-merge(SYFM_Tweets_2018, SYFM_SP_2018, by = "date", all.x = TRUE)
write.csv(df_SYFM_2018, file = "SYFM_2018", row.names = FALSE)
df_SYFM_2016df_SYFM_2018#Southern Company
#2016
df_Southern_2016 <-merge(SouthernC_Tweets_2016, SO_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Southern_2016, file = "Southern_2016", row.names = FALSE)
#2018
df_Southern_2018 <-merge(SouthernC_Tweets_2018, SO_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Southern_2018, file = "Southern_2018", row.names = FALSE)
df_Southern_2016df_Southern_2018#Apple
#2016
df_Apple_2016 <-merge(Apple_Tweets_2016, Apple_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Apple_2016, file = "Apple_2016", row.names = FALSE)
#2018
df_Apple_2018 <-merge(Apple_Tweets_2018, Apple_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Apple_2018, file = "Apple_2018", row.names = FALSE)
df_Apple_2016df_Apple_2018#XL
#2016
df_XL_2016 <-merge(XL_Tweets_2016, XL_SP_2016, by = "date", all.x = TRUE)
write.csv(df_XL_2016, file = "XL_2016", row.names = FALSE)
#2018
df_XL_2018 <-merge(XL_Tweets_2018, XL_SP_2018, by = "date", all.x = TRUE)
write.csv(df_XL_2018, file = "XL_2018", row.names = FALSE)
df_XL_2016df_XL_2018#Tysonfoods
#2016
df_Tysonf_2016 <-merge(TSN_Tweets_2016, TSN_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Tysonf_2016, file = "TSN_2016", row.names = FALSE)
#2018
df_Tysonf_2018 <-merge(TSN_Tweets_2018, TSN_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Tysonf_2018, file = "TSN_2018", row.names = FALSE)
df_Tysonf_2016df_Tysonf_2018#Akamai
#2016
df_Akamai_2016 <-merge(Akamai_Tweets_2016, Akamai_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Akamai_2016, file = "Akamai_2016", row.names = FALSE)
#2018
df_Akamai_2018 <-merge(Akamai_Tweets_2018, Akamai_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Akamai_2018, file = "Akamai_2018", row.names = FALSE)
df_Akamai_2016df_Akamai_2018###Clean
#Packages
library(tm)
library(stringr)
#Function to clean the tweets
clean_tweets <- function(tweets) {
# Convert text to lower case
tweets <- tolower(tweets)
# Remove URLs
tweets <- stringr::str_replace_all(tweets, "http[^[:space:]]*", "")
# Remove mentions
tweets <- stringr::str_replace_all(tweets, "@[^[:space:]]*", "")
# Remove hashtags
tweets <- stringr::str_replace_all(tweets, "#[^[:space:]]*", "")
# Remove punctuation marks
tweets <- stringr::str_replace_all(tweets, "[[:punct:]]", "")
# Remove numbers
tweets <- stringr::str_replace_all(tweets, "[[:digit:]]", "")
# Remove common stop words
tweets <- tm::removeWords(tweets, tm::stopwords("en"))
return(tweets)
}#Lemmatize
df_Test$text <- lemmatize_words(df_Test$text)
#Numeric NA values & Tweets
#Johnson
#Remove columns
df_Johnson_2016 <- df_Johnson_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Johnson_2016
#Ticker
df_Johnson_2016$TICKER[is.na(df_Johnson_2016$TICKER)] <- "JCI"
#PRC
PRC_AVG <- mean(df_Johnson_2016$PRC, na.rm = TRUE)
df_Johnson_2016$PRC[is.na(df_Johnson_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Johnson_2016$VOL, na.rm = TRUE)
df_Johnson_2016$VOL[is.na(df_Johnson_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Johnson_2016$BID, na.rm = TRUE)
df_Johnson_2016$BID[is.na(df_Johnson_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Johnson_2016$ASK, na.rm = TRUE)
df_Johnson_2016$ASK[is.na(df_Johnson_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Johnson_2016$OPENPRC, na.rm = TRUE)
df_Johnson_2016$OPENPRC[is.na(df_Johnson_2016$OPENPRC)] <- OPEN_AVG
df_Johnson_2016df_Johnson_2016$text <- clean_tweets(df_Johnson_2016$text)
df_Johnson_2016$text <- lemmatize_words(df_Johnson_2016$text)#Remove columns
df_Johnson_2017 <- df_Johnson_2017[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Johnson_2017
#Ticker
df_Johnson_2017$TICKER[is.na(df_Johnson_2017$TICKER)] <- "JCI"
#PRC
PRC_AVG <- mean(df_Johnson_2017$PRC, na.rm = TRUE)
df_Johnson_2017$PRC[is.na(df_Johnson_2017$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Johnson_2017$VOL, na.rm = TRUE)
df_Johnson_2017$VOL[is.na(df_Johnson_2017$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Johnson_2017$BID, na.rm = TRUE)
df_Johnson_2017$BID[is.na(df_Johnson_2017$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Johnson_2017$ASK, na.rm = TRUE)
df_Johnson_2017$ASK[is.na(df_Johnson_2017$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Johnson_2017$OPENPRC, na.rm = TRUE)
df_Johnson_2017$OPENPRC[is.na(df_Johnson_2017$OPENPRC)] <- OPEN_AVG
df_Johnson_2017df_Johnson_2017$text <- clean_tweets(df_Johnson_2017$text)
df_Johnson_2017$text <- lemmatize_words(df_Johnson_2017$text)#NASDAQ
#Remove columns
df_NASDAQ <- df_NASDAQ[,-c(6,8,9,10,15,17,18,19,20,21)]
df_NASDAQ
#Ticker
df_NASDAQ$TICKER[is.na(df_NASDAQ$TICKER)] <- "NDAQ"
#PRC
PRC_AVG <- mean(df_NASDAQ$PRC, na.rm = TRUE)
df_NASDAQ$PRC[is.na(df_NASDAQ$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_NASDAQ$VOL, na.rm = TRUE)
df_NASDAQ$VOL[is.na(df_NASDAQ$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_NASDAQ$BID, na.rm = TRUE)
df_NASDAQ$BID[is.na(df_NASDAQ$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_NASDAQ$ASK, na.rm = TRUE)
df_NASDAQ$ASK[is.na(df_NASDAQ$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_NASDAQ$OPENPRC, na.rm = TRUE)
df_NASDAQ$OPENPRC[is.na(df_NASDAQ$OPENPRC)] <- OPEN_AVG
df_NASDAQdf_NASDAQ$text <- clean_tweets(df_NASDAQ$text)
df_NASDAQ$text <- lemmatize_words(df_NASDAQ$text)#Autodesk
#Remove columns
df_autodesk <- df_autodesk[,-c(6,8,9,10,15,17,18,19,20,21)]
df_autodesk
#Ticker
df_autodesk$TICKER[is.na(df_autodesk$TICKER)] <- "ADSK"
#PRC
PRC_AVG <- mean(df_autodesk$PRC, na.rm = TRUE)
df_autodesk$PRC[is.na(df_autodesk$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_autodesk$VOL, na.rm = TRUE)
df_autodesk$VOL[is.na(df_autodesk$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_autodesk$BID, na.rm = TRUE)
df_autodesk$BID[is.na(df_autodesk$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_autodesk$ASK, na.rm = TRUE)
df_autodesk$ASK[is.na(df_autodesk$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_autodesk$OPENPRC, na.rm = TRUE)
df_autodesk$OPENPRC[is.na(df_autodesk$OPENPRC)] <- OPEN_AVG
df_autodeskdf_autodesk$text <- clean_tweets(df_autodesk$text)
df_autodesk$text <- lemmatize_words(df_autodesk$text)#Hasbro
#Remove columns
df_Hasbro_2016 <- df_Hasbro_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Hasbro_2016
#Ticker
df_Hasbro_2016$TICKER[is.na(df_Hasbro_2016$TICKER)] <- "HAS"
#PRC
PRC_AVG <- mean(df_Hasbro_2016$PRC, na.rm = TRUE)
df_Hasbro_2016$PRC[is.na(df_Hasbro_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Hasbro_2016$VOL, na.rm = TRUE)
df_Hasbro_2016$VOL[is.na(df_Hasbro_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Hasbro_2016$BID, na.rm = TRUE)
df_Hasbro_2016$BID[is.na(df_Hasbro_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Hasbro_2016$ASK, na.rm = TRUE)
df_Hasbro_2016$ASK[is.na(df_Hasbro_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Hasbro_2016$OPENPRC, na.rm = TRUE)
df_Hasbro_2016$OPENPRC[is.na(df_Hasbro_2016$OPENPRC)] <- OPEN_AVG
df_Hasbro_2016df_Hasbro_2016$text <- clean_tweets(df_Hasbro_2016$text)
df_Hasbro_2016$text <- lemmatize_words(df_Hasbro_2016$text)#Remove columns
df_Hasbro_2018 <- df_Hasbro_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Hasbro_2018
#Ticker
df_Hasbro_2018$TICKER[is.na(df_Hasbro_2018$TICKER)] <- "HAS"
#PRC
PRC_AVG <- mean(df_Hasbro_2018$PRC, na.rm = TRUE)
df_Hasbro_2018$PRC[is.na(df_Hasbro_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Hasbro_2018$VOL, na.rm = TRUE)
df_Hasbro_2018$VOL[is.na(df_Hasbro_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Hasbro_2018$BID, na.rm = TRUE)
df_Hasbro_2018$BID[is.na(df_Hasbro_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Hasbro_2018$ASK, na.rm = TRUE)
df_Hasbro_2018$ASK[is.na(df_Hasbro_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Hasbro_2018$OPENPRC, na.rm = TRUE)
df_Hasbro_2018$OPENPRC[is.na(df_Hasbro_2018$OPENPRC)] <- OPEN_AVG
df_Hasbro_2018df_Hasbro_2018$text <- clean_tweets(df_Hasbro_2018$text)
df_Hasbro_2018$text <- lemmatize_words(df_Hasbro_2018$text)#Intel
#Remove columns
df_Intel_2016 <- df_Intel_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Intel_2016
#Ticker
df_Intel_2016$TICKER[is.na(df_Intel_2016$TICKER)] <- "INTC"
#PRC
PRC_AVG <- mean(df_Intel_2016$PRC, na.rm = TRUE)
df_Intel_2016$PRC[is.na(df_Intel_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Intel_2016$VOL, na.rm = TRUE)
df_Intel_2016$VOL[is.na(df_Intel_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Intel_2016$BID, na.rm = TRUE)
df_Intel_2016$BID[is.na(df_Intel_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Intel_2016$ASK, na.rm = TRUE)
df_Intel_2016$ASK[is.na(df_Intel_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Intel_2016$OPENPRC, na.rm = TRUE)
df_Intel_2016$OPENPRC[is.na(df_Intel_2016$OPENPRC)] <- OPEN_AVG
df_Intel_2016df_Intel_2016$text <- clean_tweets(df_Intel_2016$text)
df_Intel_2016$text <- lemmatize_words(df_Intel_2016$text)#Remove columns
df_Intel_2018 <- df_Intel_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Intel_2018
#Ticker
df_Intel_2018$TICKER[is.na(df_Intel_2018$TICKER)] <- "INTC"
#PRC
PRC_AVG <- mean(df_Intel_2018$PRC, na.rm = TRUE)
df_Intel_2018$PRC[is.na(df_Intel_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Intel_2018$VOL, na.rm = TRUE)
df_Intel_2018$VOL[is.na(df_Intel_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Intel_2018$BID, na.rm = TRUE)
df_Intel_2018$BID[is.na(df_Intel_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Intel_2018$ASK, na.rm = TRUE)
df_Intel_2018$ASK[is.na(df_Intel_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Intel_2018$OPENPRC, na.rm = TRUE)
df_Intel_2018$OPENPRC[is.na(df_Intel_2018$OPENPRC)] <- OPEN_AVG
df_Intel_2018df_Intel_2018$text <- clean_tweets(df_Intel_2018$text)
df_Intel_2018$text <- lemmatize_words(df_Intel_2018$text)#Activision
#Remove columns
df_Activision_2016 <- df_Activision_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Activision_2016
#Ticker
df_Activision_2016$TICKER[is.na(df_Activision_2016$TICKER)] <- "ATVI"
#PRC
PRC_AVG <- mean(df_Activision_2016$PRC, na.rm = TRUE)
df_Activision_2016$PRC[is.na(df_Activision_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Activision_2016$VOL, na.rm = TRUE)
df_Activision_2016$VOL[is.na(df_Activision_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Activision_2016$BID, na.rm = TRUE)
df_Activision_2016$BID[is.na(df_Activision_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Activision_2016$ASK, na.rm = TRUE)
df_Activision_2016$ASK[is.na(df_Activision_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Activision_2016$OPENPRC, na.rm = TRUE)
df_Activision_2016$OPENPRC[is.na(df_Activision_2016$OPENPRC)] <- OPEN_AVG
df_Activision_2016df_Activision_2016$text <- clean_tweets(df_Activision_2016$text)
df_Activision_2016$text <- lemmatize_words(df_Activision_2016$text)#Remove columns
df_Activision_2018 <- df_Activision_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Activision_2018
#Ticker
df_Activision_2018$TICKER[is.na(df_Activision_2018$TICKER)] <- "ATVI"
#PRC
PRC_AVG <- mean(df_Activision_2018$PRC, na.rm = TRUE)
df_Activision_2018$PRC[is.na(df_Activision_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Activision_2018$VOL, na.rm = TRUE)
df_Activision_2018$VOL[is.na(df_Activision_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Activision_2018$BID, na.rm = TRUE)
df_Activision_2018$BID[is.na(df_Activision_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Activision_2018$ASK, na.rm = TRUE)
df_Activision_2018$ASK[is.na(df_Activision_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Activision_2018$OPENPRC, na.rm = TRUE)
df_Activision_2018$OPENPRC[is.na(df_Activision_2018$OPENPRC)] <- OPEN_AVG
df_Activision_2018df_Activision_2018$text <- clean_tweets(df_Activision_2018$text)
df_Activision_2018$text <- lemmatize_words(df_Activision_2018$text)#Inuit
#Remove columns
df_Inuit_2016 <- df_Inuit_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Inuit_2016
#Ticker
df_Inuit_2016$TICKER[is.na(df_Inuit_2016$TICKER)] <- "INTU"
#PRC
PRC_AVG <- mean(df_Inuit_2016$PRC, na.rm = TRUE)
df_Inuit_2016$PRC[is.na(df_Inuit_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Inuit_2016$VOL, na.rm = TRUE)
df_Inuit_2016$VOL[is.na(df_Inuit_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Inuit_2016$BID, na.rm = TRUE)
df_Inuit_2016$BID[is.na(df_Inuit_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Inuit_2016$ASK, na.rm = TRUE)
df_Inuit_2016$ASK[is.na(df_Inuit_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Inuit_2016$OPENPRC, na.rm = TRUE)
df_Inuit_2016$OPENPRC[is.na(df_Inuit_2016$OPENPRC)] <- OPEN_AVG
df_Inuit_2016df_Inuit_2016$text <- clean_tweets(df_Inuit_2016$text)
df_Inuit_2016$text <- lemmatize_words(df_Inuit_2016$text)#Remove columns
df_Inuit_2018 <- df_Inuit_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Inuit_2018
#Ticker
df_Inuit_2018$TICKER[is.na(df_Inuit_2018$TICKER)] <- "INTU"
#PRC
PRC_AVG <- mean(df_Inuit_2018$PRC, na.rm = TRUE)
df_Inuit_2018$PRC[is.na(df_Inuit_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Inuit_2018$VOL, na.rm = TRUE)
df_Inuit_2018$VOL[is.na(df_Inuit_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Inuit_2018$BID, na.rm = TRUE)
df_Inuit_2018$BID[is.na(df_Inuit_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Inuit_2018$ASK, na.rm = TRUE)
df_Inuit_2018$ASK[is.na(df_Inuit_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Inuit_2018$OPENPRC, na.rm = TRUE)
df_Inuit_2018$OPENPRC[is.na(df_Inuit_2018$OPENPRC)] <- OPEN_AVG
df_Inuit_2018df_Inuit_2018$text <- clean_tweets(df_Inuit_2018$text)
df_Inuit_2018$text <- lemmatize_words(df_Inuit_2018$text)#Allergan
#Remove columns
df_Allergan_2016 <- df_Allergan_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Allergan_2016
#Ticker
df_Allergan_2016$TICKER[is.na(df_Allergan_2016$TICKER)] <- "AGN"
#PRC
PRC_AVG <- mean(df_Allergan_2016$PRC, na.rm = TRUE)
df_Allergan_2016$PRC[is.na(df_Allergan_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Allergan_2016$VOL, na.rm = TRUE)
df_Allergan_2016$VOL[is.na(df_Allergan_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Allergan_2016$BID, na.rm = TRUE)
df_Allergan_2016$BID[is.na(df_Allergan_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Allergan_2016$ASK, na.rm = TRUE)
df_Allergan_2016$ASK[is.na(df_Allergan_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Allergan_2016$OPENPRC, na.rm = TRUE)
df_Allergan_2016$OPENPRC[is.na(df_Allergan_2016$OPENPRC)] <- OPEN_AVG
df_Allergan_2016df_Allergan_2016$text <- clean_tweets(df_Allergan_2016$text)
df_Allergan_2016$text <- lemmatize_words(df_Allergan_2016$text)#Remove columns
df_Allergan_2018 <- df_Allergan_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Allergan_2018
#Ticker
df_Allergan_2018$TICKER[is.na(df_Allergan_2018$TICKER)] <- "AGN"
#PRC
PRC_AVG <- mean(df_Allergan_2018$PRC, na.rm = TRUE)
df_Allergan_2018$PRC[is.na(df_Allergan_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Allergan_2018$VOL, na.rm = TRUE)
df_Allergan_2018$VOL[is.na(df_Allergan_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Allergan_2018$BID, na.rm = TRUE)
df_Allergan_2018$BID[is.na(df_Allergan_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Allergan_2018$ASK, na.rm = TRUE)
df_Allergan_2018$ASK[is.na(df_Allergan_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Allergan_2018$OPENPRC, na.rm = TRUE)
df_Allergan_2018$OPENPRC[is.na(df_Allergan_2018$OPENPRC)] <- OPEN_AVG
df_Allergan_2018df_Allergan_2018$text <- clean_tweets(df_Allergan_2018$text)
df_Allergan_2018$text <- lemmatize_words(df_Allergan_2018$text)#Humana
#Remove columns
df_Humana_2016 <- df_Humana_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Humana_2016
#Ticker
df_Humana_2016$TICKER[is.na(df_Humana_2016$TICKER)] <- "HUM"
#PRC
PRC_AVG <- mean(df_Humana_2016$PRC, na.rm = TRUE)
df_Humana_2016$PRC[is.na(df_Humana_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Humana_2016$VOL, na.rm = TRUE)
df_Humana_2016$VOL[is.na(df_Humana_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Humana_2016$BID, na.rm = TRUE)
df_Humana_2016$BID[is.na(df_Humana_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Humana_2016$ASK, na.rm = TRUE)
df_Humana_2016$ASK[is.na(df_Humana_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Humana_2016$OPENPRC, na.rm = TRUE)
df_Humana_2016$OPENPRC[is.na(df_Humana_2016$OPENPRC)] <- OPEN_AVG
df_Humana_2016df_Humana_2016$text <- clean_tweets(df_Humana_2016$text)
df_Humana_2016$text <- lemmatize_words(df_Humana_2016$text)#Remove columns
df_Humana_2018 <- df_Humana_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Humana_2018
#Ticker
df_Humana_2018$TICKER[is.na(df_Humana_2018$TICKER)] <- "HUM"
#PRC
PRC_AVG <- mean(df_Humana_2018$PRC, na.rm = TRUE)
df_Humana_2018$PRC[is.na(df_Humana_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Humana_2018$VOL, na.rm = TRUE)
df_Humana_2018$VOL[is.na(df_Humana_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Humana_2018$BID, na.rm = TRUE)
df_Humana_2018$BID[is.na(df_Humana_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Humana_2018$ASK, na.rm = TRUE)
df_Humana_2018$ASK[is.na(df_Humana_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Humana_2018$OPENPRC, na.rm = TRUE)
df_Humana_2018$OPENPRC[is.na(df_Humana_2018$OPENPRC)] <- OPEN_AVG
df_Humana_2018
df_Humana_2018$text <- clean_tweets(df_Humana_2018$text)
df_Humana_2018$text <- lemmatize_words(df_Humana_2018$text)#CB Autodesk
#Remove columns
df_CB_autodesk_2016 <- df_CB_autodesk_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_CB_autodesk_2016
#Ticker
df_CB_autodesk_2016$TICKER[is.na(df_CB_autodesk_2016$TICKER)] <- "ADSK"
#PRC
PRC_AVG <- mean(df_CB_autodesk_2016$PRC, na.rm = TRUE)
df_CB_autodesk_2016$PRC[is.na(df_CB_autodesk_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_CB_autodesk_2016$VOL, na.rm = TRUE)
df_CB_autodesk_2016$VOL[is.na(df_CB_autodesk_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_CB_autodesk_2016$BID, na.rm = TRUE)
df_CB_autodesk_2016$BID[is.na(df_CB_autodesk_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_CB_autodesk_2016$ASK, na.rm = TRUE)
df_CB_autodesk_2016$ASK[is.na(df_CB_autodesk_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CB_autodesk_2016$OPENPRC, na.rm = TRUE)
df_CB_autodesk_2016$OPENPRC[is.na(df_CB_autodesk_2016$OPENPRC)] <- OPEN_AVG
df_CB_autodesk_2016df_CB_autodesk_2016$text <- clean_tweets(df_CB_autodesk_2016$text)
df_CB_autodesk_2016$text <- lemmatize_words(df_CB_autodesk_2016$text)#Remove columns
df_CB_autodesk_2018 <- df_CB_autodesk_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_CB_autodesk_2018
#Ticker
df_CB_autodesk_2018$TICKER[is.na(df_CB_autodesk_2018$TICKER)] <- "ADSK"
#PRC
PRC_AVG <- mean(df_CB_autodesk_2018$PRC, na.rm = TRUE)
df_CB_autodesk_2018$PRC[is.na(df_CB_autodesk_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_CB_autodesk_2018$VOL, na.rm = TRUE)
df_CB_autodesk_2018$VOL[is.na(df_CB_autodesk_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_CB_autodesk_2018$BID, na.rm = TRUE)
df_CB_autodesk_2018$BID[is.na(df_CB_autodesk_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_CB_autodesk_2018$ASK, na.rm = TRUE)
df_CB_autodesk_2018$ASK[is.na(df_CB_autodesk_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CB_autodesk_2018$OPENPRC, na.rm = TRUE)
df_CB_autodesk_2018$OPENPRC[is.na(df_CB_autodesk_2018$OPENPRC)] <- OPEN_AVG
df_CB_autodesk_2018df_CB_autodesk_2018$text <- clean_tweets(df_CB_autodesk_2018$text)
df_CB_autodesk_2018$text <- lemmatize_words(df_CB_autodesk_2018$text)#Equinox
#Remove columns
df_equinox_2018 <- df_equinox_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_equinox_2018
#Ticker
df_equinox_2018$TICKER[is.na(df_equinox_2018$TICKER)] <- "EQIX"
#PRC
PRC_AVG <- mean(df_equinox_2018$PRC, na.rm = TRUE)
df_equinox_2018$PRC[is.na(df_equinox_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_equinox_2018$VOL, na.rm = TRUE)
df_equinox_2018$VOL[is.na(df_equinox_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_equinox_2018$BID, na.rm = TRUE)
df_equinox_2018$BID[is.na(df_equinox_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_equinox_2018$ASK, na.rm = TRUE)
df_equinox_2018$ASK[is.na(df_equinox_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_equinox_2018$OPENPRC, na.rm = TRUE)
df_equinox_2018$OPENPRC[is.na(df_equinox_2018$OPENPRC)] <- OPEN_AVG
df_equinox_2018df_equinox_2018$text <- clean_tweets(df_equinox_2018$text)
df_equinox_2018$text <- lemmatize_words(df_equinox_2018$text)#Cisco
#Remove columns
df_cisco_2016 <- df_cisco_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_cisco_2016
#Ticker
df_cisco_2016$TICKER[is.na(df_cisco_2016$TICKER)] <- "CSCO"
#PRC
PRC_AVG <- mean(df_cisco_2016$PRC, na.rm = TRUE)
df_cisco_2016$PRC[is.na(df_cisco_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_cisco_2016$VOL, na.rm = TRUE)
df_cisco_2016$VOL[is.na(df_cisco_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_cisco_2016$BID, na.rm = TRUE)
df_cisco_2016$BID[is.na(df_cisco_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_cisco_2016$ASK, na.rm = TRUE)
df_cisco_2016$ASK[is.na(df_cisco_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_cisco_2016$OPENPRC, na.rm = TRUE)
df_cisco_2016$OPENPRC[is.na(df_cisco_2016$OPENPRC)] <- OPEN_AVG
df_cisco_2016df_cisco_2016$text <- clean_tweets(df_cisco_2016$text)
df_cisco_2016$text <- lemmatize_words(df_cisco_2016$text)#Remove columns
df_cisco_2018 <- df_cisco_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_cisco_2018
#Ticker
df_cisco_2018$TICKER[is.na(df_cisco_2018$TICKER)] <- "CSCO"
#PRC
PRC_AVG <- mean(df_cisco_2018$PRC, na.rm = TRUE)
df_cisco_2018$PRC[is.na(df_cisco_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_cisco_2018$VOL, na.rm = TRUE)
df_cisco_2018$VOL[is.na(df_cisco_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_cisco_2018$BID, na.rm = TRUE)
df_cisco_2018$BID[is.na(df_cisco_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_cisco_2018$ASK, na.rm = TRUE)
df_cisco_2018$ASK[is.na(df_cisco_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_cisco_2018$OPENPRC, na.rm = TRUE)
df_cisco_2018$OPENPRC[is.na(df_cisco_2018$OPENPRC)] <- OPEN_AVG
df_cisco_2018df_cisco_2018$text <- clean_tweets(df_cisco_2018$text)
df_cisco_2018$text <- lemmatize_words(df_cisco_2018$text)#EBAY
#Remove columns
df_EBAY_2016 <- df_EBAY_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_EBAY_2016
#Ticker
df_EBAY_2016$TICKER[is.na(df_EBAY_2016$TICKER)] <- "EBAY"
#PRC
PRC_AVG <- mean(df_EBAY_2016$PRC, na.rm = TRUE)
df_EBAY_2016$PRC[is.na(df_EBAY_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_EBAY_2016$VOL, na.rm = TRUE)
df_EBAY_2016$VOL[is.na(df_EBAY_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_EBAY_2016$BID, na.rm = TRUE)
df_EBAY_2016$BID[is.na(df_EBAY_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_EBAY_2016$ASK, na.rm = TRUE)
df_EBAY_2016$ASK[is.na(df_EBAY_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_EBAY_2016$OPENPRC, na.rm = TRUE)
df_EBAY_2016$OPENPRC[is.na(df_EBAY_2016$OPENPRC)] <- OPEN_AVG
df_EBAY_2016df_EBAY_2016$text <- clean_tweets(df_EBAY_2016$text)
df_EBAY_2016$text <- lemmatize_words(df_EBAY_2016$text)#Remove columns
df_EBAY_2018 <- df_EBAY_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_EBAY_2018
#Ticker
df_EBAY_2018$TICKER[is.na(df_EBAY_2018$TICKER)] <- "EBAY"
#PRC
PRC_AVG <- mean(df_EBAY_2018$PRC, na.rm = TRUE)
df_EBAY_2018$PRC[is.na(df_EBAY_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_EBAY_2018$VOL, na.rm = TRUE)
df_EBAY_2018$VOL[is.na(df_EBAY_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_EBAY_2018$BID, na.rm = TRUE)
df_EBAY_2018$BID[is.na(df_EBAY_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_EBAY_2018$ASK, na.rm = TRUE)
df_EBAY_2018$ASK[is.na(df_EBAY_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_EBAY_2018$OPENPRC, na.rm = TRUE)
df_EBAY_2018$OPENPRC[is.na(df_EBAY_2018$OPENPRC)] <- OPEN_AVG
df_EBAY_2018df_EBAY_2018$text <- clean_tweets(df_EBAY_2018$text)
df_EBAY_2018$text <- lemmatize_words(df_EBAY_2018$text)#Davita
#Remove columns
df_Davita_2016 <- df_Davita_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Davita_2016
#Ticker
df_Davita_2016$TICKER[is.na(df_Davita_2016$TICKER)] <- "DVA"
#PRC
PRC_AVG <- mean(df_Davita_2016$PRC, na.rm = TRUE)
df_Davita_2016$PRC[is.na(df_Davita_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Davita_2016$VOL, na.rm = TRUE)
df_Davita_2016$VOL[is.na(df_Davita_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Davita_2016$BID, na.rm = TRUE)
df_Davita_2016$BID[is.na(df_Davita_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Davita_2016$ASK, na.rm = TRUE)
df_Davita_2016$ASK[is.na(df_Davita_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Davita_2016$OPENPRC, na.rm = TRUE)
df_Davita_2016$OPENPRC[is.na(df_Davita_2016$OPENPRC)] <- OPEN_AVG
df_Davita_2016df_Davita_2016$text <- clean_tweets(df_Davita_2016$text)
df_Davita_2016$text <- lemmatize_words(df_Davita_2016$text)#Remove columns
df_Davita_2017 <- df_Davita_2017[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Davita_2017
#Ticker
df_Davita_2017$TICKER[is.na(df_Davita_2017$TICKER)] <- "DVA"
#PRC
PRC_AVG <- mean(df_Davita_2017$PRC, na.rm = TRUE)
df_Davita_2017$PRC[is.na(df_Davita_2017$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Davita_2017$VOL, na.rm = TRUE)
df_Davita_2017$VOL[is.na(df_Davita_2017$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Davita_2017$BID, na.rm = TRUE)
df_Davita_2017$BID[is.na(df_Davita_2017$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Davita_2017$ASK, na.rm = TRUE)
df_Davita_2017$ASK[is.na(df_Davita_2017$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Davita_2017$OPENPRC, na.rm = TRUE)
df_Davita_2017$OPENPRC[is.na(df_Davita_2017$OPENPRC)] <- OPEN_AVG
df_Davita_2017df_Davita_2017$text <- clean_tweets(df_Davita_2017$text)
df_Davita_2017$text <- lemmatize_words(df_Davita_2017$text)#Illumina
#Remove columns
df_Illumina_2016 <- df_Illumina_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Illumina_2016
#Ticker
df_Illumina_2016$TICKER[is.na(df_Illumina_2016$TICKER)] <- "ILMN"
#PRC
PRC_AVG <- mean(df_Illumina_2016$PRC, na.rm = TRUE)
df_Illumina_2016$PRC[is.na(df_Illumina_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Illumina_2016$VOL, na.rm = TRUE)
df_Illumina_2016$VOL[is.na(df_Illumina_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Illumina_2016$BID, na.rm = TRUE)
df_Illumina_2016$BID[is.na(df_Illumina_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Illumina_2016$ASK, na.rm = TRUE)
df_Illumina_2016$ASK[is.na(df_Illumina_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Illumina_2016$OPENPRC, na.rm = TRUE)
df_Illumina_2016$OPENPRC[is.na(df_Illumina_2016$OPENPRC)] <- OPEN_AVG
df_Illumina_2016df_Illumina_2016$text <- clean_tweets(df_Illumina_2016$text)
df_Illumina_2016$text <- lemmatize_words(df_Illumina_2016$text)#Remove columns
df_Illumina_2018 <- df_Illumina_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Illumina_2018
#Ticker
df_Illumina_2018$TICKER[is.na(df_Illumina_2018$TICKER)] <- "ILMN"
#PRC
PRC_AVG <- mean(df_Illumina_2018$PRC, na.rm = TRUE)
df_Illumina_2018$PRC[is.na(df_Illumina_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Illumina_2018$VOL, na.rm = TRUE)
df_Illumina_2018$VOL[is.na(df_Illumina_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Illumina_2018$BID, na.rm = TRUE)
df_Illumina_2018$BID[is.na(df_Illumina_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Illumina_2018$ASK, na.rm = TRUE)
df_Illumina_2018$ASK[is.na(df_Illumina_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Illumina_2018$OPENPRC, na.rm = TRUE)
df_Illumina_2018$OPENPRC[is.na(df_Illumina_2018$OPENPRC)] <- OPEN_AVG
df_Illumina_2018df_Illumina_2018$text <- clean_tweets(df_Illumina_2018$text)
df_Illumina_2018$text <- lemmatize_words(df_Illumina_2018$text)#Homedepot
#Remove columns
df_Homedepot_2013 <- df_Homedepot_2013[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Homedepot_2013
#Ticker
df_Homedepot_2013$TICKER[is.na(df_Homedepot_2013$TICKER)] <- "HD"
#PRC
PRC_AVG <- mean(df_Homedepot_2013$PRC, na.rm = TRUE)
df_Homedepot_2013$PRC[is.na(df_Homedepot_2013$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Homedepot_2013$VOL, na.rm = TRUE)
df_Homedepot_2013$VOL[is.na(df_Homedepot_2013$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Homedepot_2013$BID, na.rm = TRUE)
df_Homedepot_2013$BID[is.na(df_Homedepot_2013$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Homedepot_2013$ASK, na.rm = TRUE)
df_Homedepot_2013$ASK[is.na(df_Homedepot_2013$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Homedepot_2013$OPENPRC, na.rm = TRUE)
df_Homedepot_2013$OPENPRC[is.na(df_Homedepot_2013$OPENPRC)] <- OPEN_AVG
df_Homedepot_2013df_Homedepot_2013$text <- clean_tweets(df_Homedepot_2013$text)
df_Homedepot_2013$text <- lemmatize_words(df_Homedepot_2013$text)#Southwest Airlines
#Remove columns
df_Southwest_2016 <- df_Southwest_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Southwest_2016
#Ticker
df_Southwest_2016$TICKER[is.na(df_Southwest_2016$TICKER)] <- "LUV"
#PRC
PRC_AVG <- mean(df_Southwest_2016$PRC, na.rm = TRUE)
df_Southwest_2016$PRC[is.na(df_Southwest_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Southwest_2016$VOL, na.rm = TRUE)
df_Southwest_2016$VOL[is.na(df_Southwest_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Southwest_2016$BID, na.rm = TRUE)
df_Southwest_2016$BID[is.na(df_Southwest_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southwest_2016$ASK, na.rm = TRUE)
df_Southwest_2016$ASK[is.na(df_Southwest_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southwest_2016$OPENPRC, na.rm = TRUE)
df_Southwest_2016$OPENPRC[is.na(df_Southwest_2016$OPENPRC)] <- OPEN_AVG
df_Southwest_2016df_Southwest_2016$text <- clean_tweets(df_Southwest_2016$text)
df_Southwest_2016$text <- lemmatize_words(df_Southwest_2016$text)#Remove columns
df_Southwest_2018 <- df_Southwest_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Southwest_2018
#Ticker
df_Southwest_2018$TICKER[is.na(df_Southwest_2018$TICKER)] <- "LUV"
#PRC
PRC_AVG <- mean(df_Southwest_2018$PRC, na.rm = TRUE)
df_Southwest_2018$PRC[is.na(df_Southwest_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Southwest_2018$VOL, na.rm = TRUE)
df_Southwest_2018$VOL[is.na(df_Southwest_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Southwest_2018$BID, na.rm = TRUE)
df_Southwest_2018$BID[is.na(df_Southwest_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southwest_2018$ASK, na.rm = TRUE)
df_Southwest_2018$ASK[is.na(df_Southwest_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southwest_2018$OPENPRC, na.rm = TRUE)
df_Southwest_2018$OPENPRC[is.na(df_Southwest_2018$OPENPRC)] <- OPEN_AVG
df_Southwest_2018df_Southwest_2018$text <- clean_tweets(df_Southwest_2018$text)
df_Southwest_2018$text <- lemmatize_words(df_Southwest_2018$text)#FIS
#Remove columns
df_FIS_2016 <- df_FIS_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_FIS_2016
#Ticker
df_FIS_2016$TICKER[is.na(df_FIS_2016$TICKER)] <- "FIS"
#PRC
PRC_AVG <- mean(df_FIS_2016$PRC, na.rm = TRUE)
df_FIS_2016$PRC[is.na(df_FIS_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_FIS_2016$VOL, na.rm = TRUE)
df_FIS_2016$VOL[is.na(df_FIS_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_FIS_2016$BID, na.rm = TRUE)
df_FIS_2016$BID[is.na(df_FIS_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_FIS_2016$ASK, na.rm = TRUE)
df_FIS_2016$ASK[is.na(df_FIS_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_FIS_2016$OPENPRC, na.rm = TRUE)
df_FIS_2016$OPENPRC[is.na(df_FIS_2016$OPENPRC)] <- OPEN_AVG
df_FIS_2016df_FIS_2016$text <- clean_tweets(df_FIS_2016$text)
df_FIS_2016$text <- lemmatize_words(df_FIS_2016$text)#Remove columns
df_FIS_2018 <- df_FIS_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_FIS_2018
#Ticker
df_FIS_2018$TICKER[is.na(df_FIS_2018$TICKER)] <- "FIS"
#PRC
PRC_AVG <- mean(df_FIS_2018$PRC, na.rm = TRUE)
df_FIS_2018$PRC[is.na(df_FIS_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_FIS_2018$VOL, na.rm = TRUE)
df_FIS_2018$VOL[is.na(df_FIS_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_FIS_2018$BID, na.rm = TRUE)
df_FIS_2018$BID[is.na(df_FIS_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_FIS_2018$ASK, na.rm = TRUE)
df_FIS_2018$ASK[is.na(df_FIS_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_FIS_2018$OPENPRC, na.rm = TRUE)
df_FIS_2018$OPENPRC[is.na(df_FIS_2018$OPENPRC)] <- OPEN_AVG
df_FIS_2018df_FIS_2018$text <- clean_tweets(df_FIS_2018$text)
df_FIS_2018$text <- lemmatize_words(df_FIS_2018$text)#Leucadia nation
#Remove columns
df_Leucadia_2016 <- df_Leucadia_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Leucadia_2016
#Ticker
df_Leucadia_2016$TICKER[is.na(df_Leucadia_2016$TICKER)] <- "LUK"
#PRC
PRC_AVG <- mean(df_Leucadia_2016$PRC, na.rm = TRUE)
df_Leucadia_2016$PRC[is.na(df_Leucadia_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Leucadia_2016$VOL, na.rm = TRUE)
df_Leucadia_2016$VOL[is.na(df_Leucadia_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Leucadia_2016$BID, na.rm = TRUE)
df_Leucadia_2016$BID[is.na(df_Leucadia_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Leucadia_2016$ASK, na.rm = TRUE)
df_Leucadia_2016$ASK[is.na(df_Leucadia_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Leucadia_2016$OPENPRC, na.rm = TRUE)
df_Leucadia_2016$OPENPRC[is.na(df_Leucadia_2016$OPENPRC)] <- OPEN_AVG
df_Leucadia_2016df_Leucadia_2016$text <- clean_tweets(df_Leucadia_2016$text)
df_Leucadia_2016$text <- lemmatize_words(df_Leucadia_2016$text)#Remove columns
df_Leucadia_2018 <- df_Leucadia_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Leucadia_2018
#Ticker
df_Leucadia_2018$TICKER[is.na(df_Leucadia_2018$TICKER)] <- "LUK"
#PRC
PRC_AVG <- mean(df_Leucadia_2018$PRC, na.rm = TRUE)
df_Leucadia_2018$PRC[is.na(df_Leucadia_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Leucadia_2018$VOL, na.rm = TRUE)
df_Leucadia_2018$VOL[is.na(df_Leucadia_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Leucadia_2018$BID, na.rm = TRUE)
df_Leucadia_2018$BID[is.na(df_Leucadia_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Leucadia_2018$ASK, na.rm = TRUE)
df_Leucadia_2018$ASK[is.na(df_Leucadia_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Leucadia_2018$OPENPRC, na.rm = TRUE)
df_Leucadia_2018$OPENPRC[is.na(df_Leucadia_2018$OPENPRC)] <- OPEN_AVG
df_Leucadia_2018df_Leucadia_2018$text <- clean_tweets(df_Leucadia_2018$text)
df_Leucadia_2018$text <- lemmatize_words(df_Leucadia_2018$text)#Verizon
#Remove columns
df_Verizon_2018 <- df_Verizon_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Verizon_2018
#Ticker
df_Verizon_2018$TICKER[is.na(df_Verizon_2018$TICKER)] <- "VZ"
#PRC
PRC_AVG <- mean(df_Verizon_2018$PRC, na.rm = TRUE)
df_Verizon_2018$PRC[is.na(df_Verizon_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Verizon_2018$VOL, na.rm = TRUE)
df_Verizon_2018$VOL[is.na(df_Verizon_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Verizon_2018$BID, na.rm = TRUE)
df_Verizon_2018$BID[is.na(df_Verizon_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Verizon_2018$ASK, na.rm = TRUE)
df_Verizon_2018$ASK[is.na(df_Verizon_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Verizon_2018$OPENPRC, na.rm = TRUE)
df_Verizon_2018$OPENPRC[is.na(df_Verizon_2018$OPENPRC)] <- OPEN_AVG
df_Verizon_2018df_Verizon_2018$text <- clean_tweets(df_Verizon_2018$text)
df_Verizon_2018$text <- lemmatize_words(df_Verizon_2018$text)#Western Union
#Remove columns
df_WU_2016 <- df_WU_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_WU_2016
#Ticker
df_WU_2016$TICKER[is.na(df_WU_2016$TICKER)] <- "WU"
#PRC
PRC_AVG <- mean(df_WU_2016$PRC, na.rm = TRUE)
df_WU_2016$PRC[is.na(df_WU_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_WU_2016$VOL, na.rm = TRUE)
df_WU_2016$VOL[is.na(df_WU_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_WU_2016$BID, na.rm = TRUE)
df_WU_2016$BID[is.na(df_WU_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_WU_2016$ASK, na.rm = TRUE)
df_WU_2016$ASK[is.na(df_WU_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WU_2016$OPENPRC, na.rm = TRUE)
df_WU_2016$OPENPRC[is.na(df_WU_2016$OPENPRC)] <- OPEN_AVG
df_WU_2016df_WU_2016$text <- clean_tweets(df_WU_2016$text)
df_WU_2016$text <- lemmatize_words(df_WU_2016$text)#Remove columns
df_WU_2018 <- df_WU_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_WU_2018
#Ticker
df_WU_2018$TICKER[is.na(df_WU_2018$TICKER)] <- "WU"
#PRC
PRC_AVG <- mean(df_WU_2018$PRC, na.rm = TRUE)
df_WU_2018$PRC[is.na(df_WU_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_WU_2018$VOL, na.rm = TRUE)
df_WU_2018$VOL[is.na(df_WU_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_WU_2018$BID, na.rm = TRUE)
df_WU_2018$BID[is.na(df_WU_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_WU_2018$ASK, na.rm = TRUE)
df_WU_2018$ASK[is.na(df_WU_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WU_2018$OPENPRC, na.rm = TRUE)
df_WU_2018$OPENPRC[is.na(df_WU_2018$OPENPRC)] <- OPEN_AVG
df_WU_2018df_WU_2018$text <- clean_tweets(df_WU_2018$text)
df_WU_2018$text <- lemmatize_words(df_WU_2018$text)#Redhat
#Remove columns
df_RedHat_2016 <- df_RedHat_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_RedHat_2016
#Ticker
df_RedHat_2016$TICKER[is.na(df_RedHat_2016$TICKER)] <- "RHT"
#PRC
PRC_AVG <- mean(df_RedHat_2016$PRC, na.rm = TRUE)
df_RedHat_2016$PRC[is.na(df_RedHat_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_RedHat_2016$VOL, na.rm = TRUE)
df_RedHat_2016$VOL[is.na(df_RedHat_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_RedHat_2016$BID, na.rm = TRUE)
df_RedHat_2016$BID[is.na(df_RedHat_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_RedHat_2016$ASK, na.rm = TRUE)
df_RedHat_2016$ASK[is.na(df_RedHat_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RedHat_2016$OPENPRC, na.rm = TRUE)
df_RedHat_2016$OPENPRC[is.na(df_RedHat_2016$OPENPRC)] <- OPEN_AVG
df_RedHat_2016df_RedHat_2016$text <- clean_tweets(df_RedHat_2016$text)
df_RedHat_2016$text <- lemmatize_words(df_RedHat_2016$text)#Remove columns
df_RedHat_2018 <- df_RedHat_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_RedHat_2018
#Ticker
df_RedHat_2018$TICKER[is.na(df_RedHat_2018$TICKER)] <- "RHT"
#PRC
PRC_AVG <- mean(df_RedHat_2018$PRC, na.rm = TRUE)
df_RedHat_2018$PRC[is.na(df_RedHat_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_RedHat_2018$VOL, na.rm = TRUE)
df_RedHat_2018$VOL[is.na(df_RedHat_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_RedHat_2018$BID, na.rm = TRUE)
df_RedHat_2018$BID[is.na(df_RedHat_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_RedHat_2018$ASK, na.rm = TRUE)
df_RedHat_2018$ASK[is.na(df_RedHat_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RedHat_2018$OPENPRC, na.rm = TRUE)
df_RedHat_2018$OPENPRC[is.na(df_RedHat_2018$OPENPRC)] <- OPEN_AVG
df_RedHat_2018df_RedHat_2018$text <- clean_tweets(df_RedHat_2018$text)
df_RedHat_2018$text <- lemmatize_words(df_RedHat_2018$text)#Amazon
#Remove columns
df_AMZN_2016 <- df_AMZN_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_AMZN_2016
#Ticker
df_AMZN_2016$TICKER[is.na(df_AMZN_2016$TICKER)] <- "AMZN"
#PRC
PRC_AVG <- mean(df_AMZN_2016$PRC, na.rm = TRUE)
df_AMZN_2016$PRC[is.na(df_AMZN_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_AMZN_2016$VOL, na.rm = TRUE)
df_AMZN_2016$VOL[is.na(df_AMZN_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_AMZN_2016$BID, na.rm = TRUE)
df_AMZN_2016$BID[is.na(df_AMZN_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMZN_2016$ASK, na.rm = TRUE)
df_AMZN_2016$ASK[is.na(df_AMZN_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMZN_2016$OPENPRC, na.rm = TRUE)
df_AMZN_2016$OPENPRC[is.na(df_AMZN_2016$OPENPRC)] <- OPEN_AVG
df_AMZN_2016df_AMZN_2016$text <- clean_tweets(df_AMZN_2016$text)
df_AMZN_2016$text <- lemmatize_words(df_AMZN_2016$text)#Remove columns
df_AMZN_2018 <- df_AMZN_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_AMZN_2018
#Ticker
df_AMZN_2018$TICKER[is.na(df_AMZN_2018$TICKER)] <- "AMZN"
#PRC
PRC_AVG <- mean(df_AMZN_2018$PRC, na.rm = TRUE)
df_AMZN_2018$PRC[is.na(df_AMZN_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_AMZN_2018$VOL, na.rm = TRUE)
df_AMZN_2018$VOL[is.na(df_AMZN_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_AMZN_2018$BID, na.rm = TRUE)
df_AMZN_2018$BID[is.na(df_AMZN_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMZN_2018$ASK, na.rm = TRUE)
df_AMZN_2018$ASK[is.na(df_AMZN_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMZN_2018$OPENPRC, na.rm = TRUE)
df_AMZN_2018$OPENPRC[is.na(df_AMZN_2018$OPENPRC)] <- OPEN_AVG
df_AMZN_2018df_AMZN_2018$text <- clean_tweets(df_AMZN_2018$text)
df_AMZN_2018$text <- lemmatize_words(df_AMZN_2018$text)
#GE
#Remove columns
df_GE_2016 <- df_GE_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_GE_2016
#Ticker
df_GE_2016$TICKER[is.na(df_GE_2016$TICKER)] <- "GE"
#PRC
PRC_AVG <- mean(df_GE_2016$PRC, na.rm = TRUE)
df_GE_2016$PRC[is.na(df_GE_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_GE_2016$VOL, na.rm = TRUE)
df_GE_2016$VOL[is.na(df_GE_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_GE_2016$BID, na.rm = TRUE)
df_GE_2016$BID[is.na(df_GE_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_GE_2016$ASK, na.rm = TRUE)
df_GE_2016$ASK[is.na(df_GE_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GE_2016$OPENPRC, na.rm = TRUE)
df_GE_2016$OPENPRC[is.na(df_GE_2016$OPENPRC)] <- OPEN_AVG
df_GE_2016df_GE_2016$text <- clean_tweets(df_GE_2016$text)
df_GE_2016$text <- lemmatize_words(df_GE_2016$text)#Remove columns
df_GE_2017 <- df_GE_2017[,-c(6,8,9,10,15,17,18,19,20,21)]
df_GE_2017
#Ticker
df_GE_2017$TICKER[is.na(df_GE_2017$TICKER)] <- "GE"
#PRC
PRC_AVG <- mean(df_GE_2017$PRC, na.rm = TRUE)
df_GE_2017$PRC[is.na(df_GE_2017$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_GE_2017$VOL, na.rm = TRUE)
df_GE_2017$VOL[is.na(df_GE_2017$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_GE_2017$BID, na.rm = TRUE)
df_GE_2017$BID[is.na(df_GE_2017$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_GE_2017$ASK, na.rm = TRUE)
df_GE_2017$ASK[is.na(df_GE_2017$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GE_2017$OPENPRC, na.rm = TRUE)
df_GE_2017$OPENPRC[is.na(df_GE_2017$OPENPRC)] <- OPEN_AVG
df_GE_2017df_GE_2017$text <- clean_tweets(df_GE_2017$text)
df_GE_2017$text <- lemmatize_words(df_GE_2017$text)#Fiserv
#Remove columns
df_Fiserv_2016 <- df_Fiserv_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Fiserv_2016
#Ticker
df_Fiserv_2016$TICKER[is.na(df_Fiserv_2016$TICKER)] <- "FISV"
#PRC
PRC_AVG <- mean(df_Fiserv_2016$PRC, na.rm = TRUE)
df_Fiserv_2016$PRC[is.na(df_Fiserv_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Fiserv_2016$VOL, na.rm = TRUE)
df_Fiserv_2016$VOL[is.na(df_Fiserv_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Fiserv_2016$BID, na.rm = TRUE)
df_Fiserv_2016$BID[is.na(df_Fiserv_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Fiserv_2016$ASK, na.rm = TRUE)
df_Fiserv_2016$ASK[is.na(df_Fiserv_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Fiserv_2016$OPENPRC, na.rm = TRUE)
df_Fiserv_2016$OPENPRC[is.na(df_Fiserv_2016$OPENPRC)] <- OPEN_AVG
df_Fiserv_2016df_Fiserv_2016$text <- clean_tweets(df_Fiserv_2016$text)
df_Fiserv_2016$text <- lemmatize_words(df_Fiserv_2016$text)#Fiserv
#Remove columns
df_Fiserv_2018 <- df_Fiserv_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Fiserv_2018
#Ticker
df_Fiserv_2018$TICKER[is.na(df_Fiserv_2018$TICKER)] <- "FISV"
#PRC
PRC_AVG <- mean(df_Fiserv_2018$PRC, na.rm = TRUE)
df_Fiserv_2018$PRC[is.na(df_Fiserv_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Fiserv_2018$VOL, na.rm = TRUE)
df_Fiserv_2018$VOL[is.na(df_Fiserv_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Fiserv_2018$BID, na.rm = TRUE)
df_Fiserv_2018$BID[is.na(df_Fiserv_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Fiserv_2018$ASK, na.rm = TRUE)
df_Fiserv_2018$ASK[is.na(df_Fiserv_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Fiserv_2018$OPENPRC, na.rm = TRUE)
df_Fiserv_2018$OPENPRC[is.na(df_Fiserv_2018$OPENPRC)] <- OPEN_AVG
df_Fiserv_2018df_Fiserv_2018$text <- clean_tweets(df_Fiserv_2018$text)
df_Fiserv_2018$text <- lemmatize_words(df_Fiserv_2018$text)#Waste Management
#Remove columns
df_WM_2018 <- df_WM_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_WM_2018
#Ticker
df_WM_2018$TICKER[is.na(df_WM_2018$TICKER)] <- "WM"
#PRC
PRC_AVG <- mean(df_WM_2018$PRC, na.rm = TRUE)
df_WM_2018$PRC[is.na(df_WM_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_WM_2018$VOL, na.rm = TRUE)
df_WM_2018$VOL[is.na(df_WM_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_WM_2018$BID, na.rm = TRUE)
df_WM_2018$BID[is.na(df_WM_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_WM_2018$ASK, na.rm = TRUE)
df_WM_2018$ASK[is.na(df_WM_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WM_2018$OPENPRC, na.rm = TRUE)
df_WM_2018$OPENPRC[is.na(df_WM_2018$OPENPRC)] <- OPEN_AVG
df_WM_2018df_WM_2018$text <- clean_tweets(df_WM_2018$text)
df_WM_2018$text <- lemmatize_words(df_WM_2018$text)#WillsTower
#Remove columns
df_Wills_2016 <- df_Wills_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Wills_2016
#Ticker
df_Wills_2016$TICKER[is.na(df_Wills_2016$TICKER)] <- "WLTW"
#PRC
PRC_AVG <- mean(df_Wills_2016$PRC, na.rm = TRUE)
df_Wills_2016$PRC[is.na(df_Wills_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Wills_2016$VOL, na.rm = TRUE)
df_Wills_2016$VOL[is.na(df_Wills_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Wills_2016$BID, na.rm = TRUE)
df_Wills_2016$BID[is.na(df_Wills_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Wills_2016$ASK, na.rm = TRUE)
df_Wills_2016$ASK[is.na(df_Wills_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Wills_2016$OPENPRC, na.rm = TRUE)
df_Wills_2016$OPENPRC[is.na(df_Wills_2016$OPENPRC)] <- OPEN_AVG
df_Wills_2016df_Wills_2016$text <- clean_tweets(df_Wills_2016$text)
df_Wills_2016$text <- lemmatize_words(df_Wills_2016$text)#Remove columns
df_Wills_2018 <- df_Wills_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Wills_2018
#Ticker
df_Wills_2018$TICKER[is.na(df_Wills_2018$TICKER)] <- "WLTW"
#PRC
PRC_AVG <- mean(df_Wills_2018$PRC, na.rm = TRUE)
df_Wills_2018$PRC[is.na(df_Wills_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Wills_2018$VOL, na.rm = TRUE)
df_Wills_2018$VOL[is.na(df_Wills_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Wills_2018$BID, na.rm = TRUE)
df_Wills_2018$BID[is.na(df_Wills_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Wills_2018$ASK, na.rm = TRUE)
df_Wills_2018$ASK[is.na(df_Wills_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Wills_2018$OPENPRC, na.rm = TRUE)
df_Wills_2018$OPENPRC[is.na(df_Wills_2018$OPENPRC)] <- OPEN_AVG
df_Wills_2018df_Wills_2018$text <- clean_tweets(df_Wills_2018$text)
df_Wills_2018$text <- lemmatize_words(df_Wills_2018$text)#Tripadvisor
#Remove columns
df_tripadvisor_2016 <- df_tripadvisor_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_tripadvisor_2016
#Ticker
df_tripadvisor_2016$TICKER[is.na(df_tripadvisor_2016$TICKER)] <- "TRIP"
#PRC
PRC_AVG <- mean(df_tripadvisor_2016$PRC, na.rm = TRUE)
df_tripadvisor_2016$PRC[is.na(df_tripadvisor_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_tripadvisor_2016$VOL, na.rm = TRUE)
df_tripadvisor_2016$VOL[is.na(df_tripadvisor_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_tripadvisor_2016$BID, na.rm = TRUE)
df_tripadvisor_2016$BID[is.na(df_tripadvisor_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_tripadvisor_2016$ASK, na.rm = TRUE)
df_tripadvisor_2016$ASK[is.na(df_tripadvisor_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_tripadvisor_2016$OPENPRC, na.rm = TRUE)
df_tripadvisor_2016$OPENPRC[is.na(df_tripadvisor_2016$OPENPRC)] <- OPEN_AVG
df_tripadvisor_2016df_tripadvisor_2016$text <- clean_tweets(df_tripadvisor_2016$text)
df_tripadvisor_2016$text <- lemmatize_words(df_tripadvisor_2016$text)#Remove columns
df_tripadvisor_2018 <- df_tripadvisor_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_tripadvisor_2018
#Ticker
df_tripadvisor_2018$TICKER[is.na(df_tripadvisor_2018$TICKER)] <- "TRIP"
#PRC
PRC_AVG <- mean(df_tripadvisor_2018$PRC, na.rm = TRUE)
df_tripadvisor_2018$PRC[is.na(df_tripadvisor_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_tripadvisor_2018$VOL, na.rm = TRUE)
df_tripadvisor_2018$VOL[is.na(df_tripadvisor_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_tripadvisor_2018$BID, na.rm = TRUE)
df_tripadvisor_2018$BID[is.na(df_tripadvisor_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_tripadvisor_2018$ASK, na.rm = TRUE)
df_tripadvisor_2018$ASK[is.na(df_tripadvisor_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_tripadvisor_2018$OPENPRC, na.rm = TRUE)
df_tripadvisor_2018$OPENPRC[is.na(df_tripadvisor_2018$OPENPRC)] <- OPEN_AVG
df_tripadvisor_2018df_tripadvisor_2018$text <- clean_tweets(df_tripadvisor_2018$text)
df_tripadvisor_2018$text <- lemmatize_words(df_tripadvisor_2018$text)#DavitaKent
#Remove columns
df_DavitaK_2016 <- df_DavitaK_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_DavitaK_2016
#Ticker
df_DavitaK_2016$TICKER[is.na(df_DavitaK_2016$TICKER)] <- "DVA"
#PRC
PRC_AVG <- mean(df_DavitaK_2016$PRC, na.rm = TRUE)
df_DavitaK_2016$PRC[is.na(df_DavitaK_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_DavitaK_2016$VOL, na.rm = TRUE)
df_DavitaK_2016$VOL[is.na(df_DavitaK_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_DavitaK_2016$BID, na.rm = TRUE)
df_DavitaK_2016$BID[is.na(df_DavitaK_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_DavitaK_2016$ASK, na.rm = TRUE)
df_DavitaK_2016$ASK[is.na(df_DavitaK_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_DavitaK_2016$OPENPRC, na.rm = TRUE)
df_DavitaK_2016$OPENPRC[is.na(df_DavitaK_2016$OPENPRC)] <- OPEN_AVG
df_DavitaK_2016df_DavitaK_2016$text <- clean_tweets(df_DavitaK_2016$text)
df_DavitaK_2016$text <- lemmatize_words(df_DavitaK_2016$text)#Remove columns
df_DavitaK_2018 <- df_DavitaK_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_DavitaK_2018
#Ticker
df_DavitaK_2018$TICKER[is.na(df_DavitaK_2018$TICKER)] <- "DVA"
#PRC
PRC_AVG <- mean(df_DavitaK_2018$PRC, na.rm = TRUE)
df_DavitaK_2018$PRC[is.na(df_DavitaK_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_DavitaK_2018$VOL, na.rm = TRUE)
df_DavitaK_2018$VOL[is.na(df_DavitaK_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_DavitaK_2018$BID, na.rm = TRUE)
df_DavitaK_2018$BID[is.na(df_DavitaK_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_DavitaK_2018$ASK, na.rm = TRUE)
df_DavitaK_2018$ASK[is.na(df_DavitaK_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_DavitaK_2018$OPENPRC, na.rm = TRUE)
df_DavitaK_2018$OPENPRC[is.na(df_DavitaK_2018$OPENPRC)] <- OPEN_AVG
df_DavitaK_2018df_DavitaK_2018$text <- clean_tweets(df_DavitaK_2018$text)
df_DavitaK_2018$text <- lemmatize_words(df_DavitaK_2018$text)#Starbucks
#Remove columns
df_Starbucks_2018 <- df_Starbucks_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Starbucks_2018
#Ticker
df_Starbucks_2018$TICKER[is.na(df_Starbucks_2018$TICKER)] <- "SBUX"
#PRC
PRC_AVG <- mean(df_Starbucks_2018$PRC, na.rm = TRUE)
df_Starbucks_2018$PRC[is.na(df_Starbucks_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Starbucks_2018$VOL, na.rm = TRUE)
df_Starbucks_2018$VOL[is.na(df_Starbucks_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Starbucks_2018$BID, na.rm = TRUE)
df_Starbucks_2018$BID[is.na(df_Starbucks_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Starbucks_2018$ASK, na.rm = TRUE)
df_Starbucks_2018$ASK[is.na(df_Starbucks_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Starbucks_2018$OPENPRC, na.rm = TRUE)
df_Starbucks_2018$OPENPRC[is.na(df_Starbucks_2018$OPENPRC)] <- OPEN_AVG
df_Starbucks_2018df_Starbucks_2018$text <- clean_tweets(df_Starbucks_2018$text)
df_Starbucks_2018$text <- lemmatize_words(df_Starbucks_2018$text)#McCormick
#Remove columns
df_McCormick_2018 <- df_McCormick_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_McCormick_2018
#Ticker
df_McCormick_2018$TICKER[is.na(df_McCormick_2018$TICKER)] <- "MKC"
#PRC
PRC_AVG <- mean(df_McCormick_2018$PRC, na.rm = TRUE)
df_McCormick_2018$PRC[is.na(df_McCormick_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_McCormick_2018$VOL, na.rm = TRUE)
df_McCormick_2018$VOL[is.na(df_McCormick_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_McCormick_2018$BID, na.rm = TRUE)
df_McCormick_2018$BID[is.na(df_McCormick_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_McCormick_2018$ASK, na.rm = TRUE)
df_McCormick_2018$ASK[is.na(df_McCormick_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_McCormick_2018$OPENPRC, na.rm = TRUE)
df_McCormick_2018$OPENPRC[is.na(df_McCormick_2018$OPENPRC)] <- OPEN_AVG
df_McCormick_2018df_McCormick_2018$text <- clean_tweets(df_McCormick_2018$text)
df_McCormick_2018$text <- lemmatize_words(df_McCormick_2018$text)#IHS Markit
#Remove columns
df_IHS_2018 <- df_IHS_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_IHS_2018
#Ticker
df_IHS_2018$TICKER[is.na(df_IHS_2018$TICKER)] <- "TCX"
#PRC
PRC_AVG <- mean(df_IHS_2018$PRC, na.rm = TRUE)
df_IHS_2018$PRC[is.na(df_IHS_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_IHS_2018$VOL, na.rm = TRUE)
df_IHS_2018$VOL[is.na(df_IHS_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_IHS_2018$BID, na.rm = TRUE)
df_IHS_2018$BID[is.na(df_IHS_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_IHS_2018$ASK, na.rm = TRUE)
df_IHS_2018$ASK[is.na(df_IHS_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_IHS_2018$OPENPRC, na.rm = TRUE)
df_IHS_2018$OPENPRC[is.na(df_IHS_2018$OPENPRC)] <- OPEN_AVG
df_IHS_2018df_IHS_2018$text <- clean_tweets(df_IHS_2018$text)
df_IHS_2018$text <- lemmatize_words(df_IHS_2018$text)#AMD
#Remove columns
df_AMD_2018 <- df_AMD_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_AMD_2018
#Ticker
df_AMD_2018$TICKER[is.na(df_AMD_2018$TICKER)] <- "AMD"
#PRC
PRC_AVG <- mean(df_AMD_2018$PRC, na.rm = TRUE)
df_AMD_2018$PRC[is.na(df_AMD_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_AMD_2018$VOL, na.rm = TRUE)
df_AMD_2018$VOL[is.na(df_AMD_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_AMD_2018$BID, na.rm = TRUE)
df_AMD_2018$BID[is.na(df_AMD_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMD_2018$ASK, na.rm = TRUE)
df_AMD_2018$ASK[is.na(df_AMD_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMD_2018$OPENPRC, na.rm = TRUE)
df_AMD_2018$OPENPRC[is.na(df_AMD_2018$OPENPRC)] <- OPEN_AVG
df_AMD_2018df_AMD_2018$text <- clean_tweets(df_AMD_2018$text)
df_AMD_2018$text <- lemmatize_words(df_AMD_2018$text)#ResMed
#Remove columns
df_ResMed_2016 <- df_ResMed_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_ResMed_2016
#Ticker
df_ResMed_2016$TICKER[is.na(df_ResMed_2016$TICKER)] <- "RMD"
#PRC
PRC_AVG <- mean(df_ResMed_2016$PRC, na.rm = TRUE)
df_ResMed_2016$PRC[is.na(df_ResMed_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_ResMed_2016$VOL, na.rm = TRUE)
df_ResMed_2016$VOL[is.na(df_ResMed_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_ResMed_2016$BID, na.rm = TRUE)
df_ResMed_2016$BID[is.na(df_ResMed_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_ResMed_2016$ASK, na.rm = TRUE)
df_ResMed_2016$ASK[is.na(df_ResMed_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_ResMed_2016$OPENPRC, na.rm = TRUE)
df_ResMed_2016$OPENPRC[is.na(df_ResMed_2016$OPENPRC)] <- OPEN_AVG
df_ResMed_2016df_ResMed_2016$text <- clean_tweets(df_ResMed_2016$text)
df_ResMed_2016$text <- lemmatize_words(df_ResMed_2016$text)#Remove columns
df_ResMed_2018 <- df_ResMed_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_ResMed_2018
#Ticker
df_ResMed_2018$TICKER[is.na(df_ResMed_2018$TICKER)] <- "RMD"
#PRC
PRC_AVG <- mean(df_ResMed_2018$PRC, na.rm = TRUE)
df_ResMed_2018$PRC[is.na(df_ResMed_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_ResMed_2018$VOL, na.rm = TRUE)
df_ResMed_2018$VOL[is.na(df_ResMed_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_ResMed_2018$BID, na.rm = TRUE)
df_ResMed_2018$BID[is.na(df_ResMed_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_ResMed_2018$ASK, na.rm = TRUE)
df_ResMed_2018$ASK[is.na(df_ResMed_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_ResMed_2018$OPENPRC, na.rm = TRUE)
df_ResMed_2018$OPENPRC[is.na(df_ResMed_2018$OPENPRC)] <- OPEN_AVG
df_ResMed_2018df_ResMed_2018$text <- clean_tweets(df_ResMed_2018$text)
df_ResMed_2018$text <- lemmatize_words(df_ResMed_2018$text)#CA
#Remove columns
df_CA_2016 <- df_CA_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_CA_2016
#Ticker
df_CA_2016$TICKER[is.na(df_CA_2016$TICKER)] <- "CA"
#PRC
PRC_AVG <- mean(df_CA_2016$PRC, na.rm = TRUE)
df_CA_2016$PRC[is.na(df_CA_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_CA_2016$VOL, na.rm = TRUE)
df_CA_2016$VOL[is.na(df_CA_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_CA_2016$BID, na.rm = TRUE)
df_CA_2016$BID[is.na(df_CA_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_CA_2016$ASK, na.rm = TRUE)
df_CA_2016$ASK[is.na(df_CA_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CA_2016$OPENPRC, na.rm = TRUE)
df_CA_2016$OPENPRC[is.na(df_CA_2016$OPENPRC)] <- OPEN_AVG
df_CA_2016df_CA_2016$text <- clean_tweets(df_CA_2016$text)
df_CA_2016$text <- lemmatize_words(df_CA_2016$text)#Remove columns
df_CA_2018 <- df_CA_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_CA_2018
#Ticker
df_CA_2018$TICKER[is.na(df_CA_2018$TICKER)] <- "CA"
#PRC
PRC_AVG <- mean(df_CA_2018$PRC, na.rm = TRUE)
df_CA_2018$PRC[is.na(df_CA_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_CA_2018$VOL, na.rm = TRUE)
df_CA_2018$VOL[is.na(df_CA_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_CA_2018$BID, na.rm = TRUE)
df_CA_2018$BID[is.na(df_CA_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_CA_2018$ASK, na.rm = TRUE)
df_CA_2018$ASK[is.na(df_CA_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CA_2018$OPENPRC, na.rm = TRUE)
df_CA_2018$OPENPRC[is.na(df_CA_2018$OPENPRC)] <- OPEN_AVG
df_CA_2018df_CA_2018$text <- clean_tweets(df_CA_2018$text)
df_CA_2018$text <- lemmatize_words(df_CA_2018$text)#GM
#Remove columns
df_GM_2016 <- df_GM_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_GM_2016
#Ticker
df_GM_2016$TICKER[is.na(df_GM_2016$TICKER)] <- "GM"
#PRC
PRC_AVG <- mean(df_GM_2016$PRC, na.rm = TRUE)
df_GM_2016$PRC[is.na(df_GM_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_GM_2016$VOL, na.rm = TRUE)
df_GM_2016$VOL[is.na(df_GM_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_GM_2016$BID, na.rm = TRUE)
df_GM_2016$BID[is.na(df_GM_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_GM_2016$ASK, na.rm = TRUE)
df_GM_2016$ASK[is.na(df_GM_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GM_2016$OPENPRC, na.rm = TRUE)
df_GM_2016$OPENPRC[is.na(df_GM_2016$OPENPRC)] <- OPEN_AVG
df_GM_2016df_GM_2016$text <- clean_tweets(df_GM_2016$text)
df_GM_2016$text <- lemmatize_words(df_GM_2016$text)#Remove columns
df_GM_2018 <- df_GM_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_GM_2018
#Ticker
df_GM_2018$TICKER[is.na(df_GM_2018$TICKER)] <- "GM"
#PRC
PRC_AVG <- mean(df_GM_2018$PRC, na.rm = TRUE)
df_GM_2018$PRC[is.na(df_GM_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_GM_2018$VOL, na.rm = TRUE)
df_GM_2018$VOL[is.na(df_GM_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_GM_2018$BID, na.rm = TRUE)
df_GM_2018$BID[is.na(df_GM_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_GM_2018$ASK, na.rm = TRUE)
df_GM_2018$ASK[is.na(df_GM_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GM_2018$OPENPRC, na.rm = TRUE)
df_GM_2018$OPENPRC[is.na(df_GM_2018$OPENPRC)] <- OPEN_AVG
df_GM_2018df_GM_2018$text <- clean_tweets(df_GM_2018$text)
df_GM_2018$text <- lemmatize_words(df_GM_2018$text)#Aetna
#Remove columns
df_Aetna_2016 <- df_Aetna_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Aetna_2016
#Ticker
df_Aetna_2016$TICKER[is.na(df_Aetna_2016$TICKER)] <- "AET"
#PRC
PRC_AVG <- mean(df_Aetna_2016$PRC, na.rm = TRUE)
df_Aetna_2016$PRC[is.na(df_Aetna_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Aetna_2016$VOL, na.rm = TRUE)
df_Aetna_2016$VOL[is.na(df_Aetna_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Aetna_2016$BID, na.rm = TRUE)
df_Aetna_2016$BID[is.na(df_Aetna_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Aetna_2016$ASK, na.rm = TRUE)
df_Aetna_2016$ASK[is.na(df_Aetna_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Aetna_2016$OPENPRC, na.rm = TRUE)
df_Aetna_2016$OPENPRC[is.na(df_Aetna_2016$OPENPRC)] <- OPEN_AVG
df_Aetna_2016df_Aetna_2016$text <- clean_tweets(df_Aetna_2016$text)
df_Aetna_2016$text <- lemmatize_words(df_Aetna_2016$text)#Remove columns
df_Aetna_2018 <- df_Aetna_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Aetna_2018
#Ticker
df_Aetna_2018$TICKER[is.na(df_Aetna_2018$TICKER)] <- "AET"
#PRC
PRC_AVG <- mean(df_Aetna_2018$PRC, na.rm = TRUE)
df_Aetna_2018$PRC[is.na(df_Aetna_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Aetna_2018$VOL, na.rm = TRUE)
df_Aetna_2018$VOL[is.na(df_Aetna_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Aetna_2018$BID, na.rm = TRUE)
df_Aetna_2018$BID[is.na(df_Aetna_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Aetna_2018$ASK, na.rm = TRUE)
df_Aetna_2018$ASK[is.na(df_Aetna_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Aetna_2018$OPENPRC, na.rm = TRUE)
df_Aetna_2018$OPENPRC[is.na(df_Aetna_2018$OPENPRC)] <- OPEN_AVG
df_Aetna_2018df_Aetna_2018$text <- clean_tweets(df_Aetna_2018$text)
df_Aetna_2018$text <- lemmatize_words(df_Aetna_2018$text)#NRG
#Remove columns
df_NRG_2016 <- df_NRG_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_NRG_2016
#Ticker
df_NRG_2016$TICKER[is.na(df_NRG_2016$TICKER)] <- "NRG"
#PRC
PRC_AVG <- mean(df_NRG_2016$PRC, na.rm = TRUE)
df_NRG_2016$PRC[is.na(df_NRG_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_NRG_2016$VOL, na.rm = TRUE)
df_NRG_2016$VOL[is.na(df_NRG_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_NRG_2016$BID, na.rm = TRUE)
df_NRG_2016$BID[is.na(df_NRG_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_NRG_2016$ASK, na.rm = TRUE)
df_NRG_2016$ASK[is.na(df_NRG_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_NRG_2016$OPENPRC, na.rm = TRUE)
df_NRG_2016$OPENPRC[is.na(df_NRG_2016$OPENPRC)] <- OPEN_AVG
df_NRG_2016df_NRG_2016$text <- clean_tweets(df_NRG_2016$text)
df_NRG_2016$text <- lemmatize_words(df_NRG_2016$text)#Remove columns
df_NRG_2018 <- df_NRG_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_NRG_2018
#Ticker
df_NRG_2018$TICKER[is.na(df_NRG_2018$TICKER)] <- "NRG"
#PRC
PRC_AVG <- mean(df_NRG_2018$PRC, na.rm = TRUE)
df_NRG_2018$PRC[is.na(df_NRG_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_NRG_2018$VOL, na.rm = TRUE)
df_NRG_2018$VOL[is.na(df_NRG_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_NRG_2018$BID, na.rm = TRUE)
df_NRG_2018$BID[is.na(df_NRG_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_NRG_2018$ASK, na.rm = TRUE)
df_NRG_2018$ASK[is.na(df_NRG_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_NRG_2018$OPENPRC, na.rm = TRUE)
df_NRG_2018$OPENPRC[is.na(df_NRG_2018$OPENPRC)] <- OPEN_AVG
df_NRG_2018df_NRG_2018$text <- clean_tweets(df_NRG_2018$text)
df_NRG_2018$text <- lemmatize_words(df_NRG_2018$text)#Medtronic
#Remove columns
df_Medtronic_2016 <- df_Medtronic_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Medtronic_2016
#Ticker
df_Medtronic_2016$TICKER[is.na(df_Medtronic_2016$TICKER)] <- "MDT"
#PRC
PRC_AVG <- mean(df_Medtronic_2016$PRC, na.rm = TRUE)
df_Medtronic_2016$PRC[is.na(df_Medtronic_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Medtronic_2016$VOL, na.rm = TRUE)
df_Medtronic_2016$VOL[is.na(df_Medtronic_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Medtronic_2016$BID, na.rm = TRUE)
df_Medtronic_2016$BID[is.na(df_Medtronic_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Medtronic_2016$ASK, na.rm = TRUE)
df_Medtronic_2016$ASK[is.na(df_Medtronic_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Medtronic_2016$OPENPRC, na.rm = TRUE)
df_Medtronic_2016$OPENPRC[is.na(df_Medtronic_2016$OPENPRC)] <- OPEN_AVG
df_Medtronic_2016df_Medtronic_2016$text <- clean_tweets(df_Medtronic_2016$text)
df_Medtronic_2016$text <- lemmatize_words(df_Medtronic_2016$text)#Remove columns
df_Medtronic_2018 <- df_Medtronic_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Medtronic_2018
#Ticker
df_Medtronic_2018$TICKER[is.na(df_Medtronic_2018$TICKER)] <- "MDT"
#PRC
PRC_AVG <- mean(df_Medtronic_2018$PRC, na.rm = TRUE)
df_Medtronic_2018$PRC[is.na(df_Medtronic_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Medtronic_2018$VOL, na.rm = TRUE)
df_Medtronic_2018$VOL[is.na(df_Medtronic_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Medtronic_2018$BID, na.rm = TRUE)
df_Medtronic_2018$BID[is.na(df_Medtronic_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Medtronic_2018$ASK, na.rm = TRUE)
df_Medtronic_2018$ASK[is.na(df_Medtronic_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Medtronic_2018$OPENPRC, na.rm = TRUE)
df_Medtronic_2018$OPENPRC[is.na(df_Medtronic_2018$OPENPRC)] <- OPEN_AVG
df_Medtronic_2018df_Medtronic_2018$text <- clean_tweets(df_Medtronic_2018$text)
df_Medtronic_2018$text <- lemmatize_words(df_Medtronic_2018$text)#Juniper
#Remove columns
df_Juniper_2016 <- df_Juniper_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Juniper_2016
#Ticker
df_Juniper_2016$TICKER[is.na(df_Juniper_2016$TICKER)] <- "JNPR"
#PRC
PRC_AVG <- mean(df_Juniper_2016$PRC, na.rm = TRUE)
df_Juniper_2016$PRC[is.na(df_Juniper_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Juniper_2016$VOL, na.rm = TRUE)
df_Juniper_2016$VOL[is.na(df_Juniper_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_2016$BID, na.rm = TRUE)
df_Juniper_2016$BID[is.na(df_Juniper_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_2016$ASK, na.rm = TRUE)
df_Juniper_2016$ASK[is.na(df_Juniper_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_2016$OPENPRC, na.rm = TRUE)
df_Juniper_2016$OPENPRC[is.na(df_Juniper_2016$OPENPRC)] <- OPEN_AVG
df_Juniper_2016df_Juniper_2016$text <- clean_tweets(df_Juniper_2016$text)
df_Juniper_2016$text <- lemmatize_words(df_Juniper_2016$text)#Remove columns
df_Juniper_2018 <- df_Juniper_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Juniper_2018
#Ticker
df_Juniper_2018$TICKER[is.na(df_Juniper_2018$TICKER)] <- "JNPR"
#PRC
PRC_AVG <- mean(df_Juniper_2018$PRC, na.rm = TRUE)
df_Juniper_2018$PRC[is.na(df_Juniper_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Juniper_2018$VOL, na.rm = TRUE)
df_Juniper_2018$VOL[is.na(df_Juniper_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_2018$BID, na.rm = TRUE)
df_Juniper_2018$BID[is.na(df_Juniper_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_2018$ASK, na.rm = TRUE)
df_Juniper_2018$ASK[is.na(df_Juniper_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_2018$OPENPRC, na.rm = TRUE)
df_Juniper_2018$OPENPRC[is.na(df_Juniper_2018$OPENPRC)] <- OPEN_AVG
df_Juniper_2018df_Juniper_2018$text <- clean_tweets(df_Juniper_2018$text)
df_Juniper_2018$text <- lemmatize_words(df_Juniper_2018$text)#AetnaR
df_AetnaR_2018 <- df_AetnaR_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_AetnaR_2018
#Ticker
df_AetnaR_2018$TICKER[is.na(df_AetnaR_2018$TICKER)] <- "AET"
#PRC
PRC_AVG <- mean(df_AetnaR_2018$PRC, na.rm = TRUE)
df_AetnaR_2018$PRC[is.na(df_AetnaR_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_AetnaR_2018$VOL, na.rm = TRUE)
df_AetnaR_2018$VOL[is.na(df_AetnaR_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_AetnaR_2018$BID, na.rm = TRUE)
df_AetnaR_2018$BID[is.na(df_AetnaR_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_AetnaR_2018$ASK, na.rm = TRUE)
df_AetnaR_2018$ASK[is.na(df_AetnaR_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AetnaR_2018$OPENPRC, na.rm = TRUE)
df_AetnaR_2018$OPENPRC[is.na(df_AetnaR_2018$OPENPRC)] <- OPEN_AVG
df_AetnaR_2018df_AetnaR_2018$text <- clean_tweets(df_AetnaR_2018$text)
df_AetnaR_2018$text <- lemmatize_words(df_AetnaR_2018$text)#Netflix
df_Netflix_2016 <- df_Netflix_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Netflix_2016
#Ticker
df_Netflix_2016$TICKER[is.na(df_Netflix_2016$TICKER)] <- "NFLX"
#PRC
PRC_AVG <- mean(df_Netflix_2016$PRC, na.rm = TRUE)
df_Netflix_2016$PRC[is.na(df_Netflix_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Netflix_2016$VOL, na.rm = TRUE)
df_Netflix_2016$VOL[is.na(df_Netflix_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Netflix_2016$BID, na.rm = TRUE)
df_Netflix_2016$BID[is.na(df_Netflix_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Netflix_2016$ASK, na.rm = TRUE)
df_Netflix_2016$ASK[is.na(df_Netflix_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Netflix_2016$OPENPRC, na.rm = TRUE)
df_Netflix_2016$OPENPRC[is.na(df_Netflix_2016$OPENPRC)] <- OPEN_AVG
df_Netflix_2016df_Netflix_2016$text <- clean_tweets(df_Netflix_2016$text)
df_Netflix_2016$text <- lemmatize_words(df_Netflix_2016$text)#Disney
df_Disney_2018 <- df_Disney_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Disney_2018
#Ticker
df_Disney_2018$TICKER[is.na(df_Disney_2018$TICKER)] <- "DIS"
#PRC
PRC_AVG <- mean(df_Disney_2018$PRC, na.rm = TRUE)
df_Disney_2018$PRC[is.na(df_Disney_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Disney_2018$VOL, na.rm = TRUE)
df_Disney_2018$VOL[is.na(df_Disney_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Disney_2018$BID, na.rm = TRUE)
df_Disney_2018$BID[is.na(df_Disney_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Disney_2018$ASK, na.rm = TRUE)
df_Disney_2018$ASK[is.na(df_Disney_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Disney_2018$OPENPRC, na.rm = TRUE)
df_Disney_2018$OPENPRC[is.na(df_Disney_2018$OPENPRC)] <- OPEN_AVG
df_Disney_2018df_Disney_2018$text <- clean_tweets(df_Disney_2018$text)
df_Disney_2018$text <- lemmatize_words(df_Disney_2018$text)#Fox
df_Fox_2015 <- df_Fox_2015[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Fox_2015
#Ticker
df_Fox_2015$TICKER[is.na(df_Fox_2015$TICKER)] <- "FOX"
#PRC
PRC_AVG <- mean(df_Fox_2015$PRC, na.rm = TRUE)
df_Fox_2015$PRC[is.na(df_Fox_2015$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Fox_2015$VOL, na.rm = TRUE)
df_Fox_2015$VOL[is.na(df_Fox_2015$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Fox_2015$BID, na.rm = TRUE)
df_Fox_2015$BID[is.na(df_Fox_2015$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Fox_2015$ASK, na.rm = TRUE)
df_Fox_2015$ASK[is.na(df_Fox_2015$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Fox_2015$OPENPRC, na.rm = TRUE)
df_Fox_2015$OPENPRC[is.na(df_Fox_2015$OPENPRC)] <- OPEN_AVG
df_Fox_2015df_Fox_2015$text <- clean_tweets(df_Fox_2015$text)
df_Fox_2015$text <- lemmatize_words(df_Fox_2015$text)#Microsoft
df_Microsoft_2016 <- df_Microsoft_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Microsoft_2016
#Ticker
df_Microsoft_2016$TICKER[is.na(df_Microsoft_2016$TICKER)] <- "MSFT"
#PRC
PRC_AVG <- mean(df_Microsoft_2016$PRC, na.rm = TRUE)
df_Microsoft_2016$PRC[is.na(df_Microsoft_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Microsoft_2016$VOL, na.rm = TRUE)
df_Microsoft_2016$VOL[is.na(df_Microsoft_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Microsoft_2016$BID, na.rm = TRUE)
df_Microsoft_2016$BID[is.na(df_Microsoft_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Microsoft_2016$ASK, na.rm = TRUE)
df_Microsoft_2016$ASK[is.na(df_Microsoft_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Microsoft_2016$OPENPRC, na.rm = TRUE)
df_Microsoft_2016$OPENPRC[is.na(df_Microsoft_2016$OPENPRC)] <- OPEN_AVG
df_Microsoft_2016df_Microsoft_2016$text <- clean_tweets(df_Microsoft_2016$text)
df_Microsoft_2016$text <- lemmatize_words(df_Microsoft_2016$text)df_Microsoft_2018 <- df_Microsoft_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Microsoft_2018
#Ticker
df_Microsoft_2018$TICKER[is.na(df_Microsoft_2018$TICKER)] <- "MSFT"
#PRC
PRC_AVG <- mean(df_Microsoft_2018$PRC, na.rm = TRUE)
df_Microsoft_2018$PRC[is.na(df_Microsoft_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Microsoft_2018$VOL, na.rm = TRUE)
df_Microsoft_2018$VOL[is.na(df_Microsoft_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Microsoft_2018$BID, na.rm = TRUE)
df_Microsoft_2018$BID[is.na(df_Microsoft_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Microsoft_2018$ASK, na.rm = TRUE)
df_Microsoft_2018$ASK[is.na(df_Microsoft_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Microsoft_2018$OPENPRC, na.rm = TRUE)
df_Microsoft_2018$OPENPRC[is.na(df_Microsoft_2018$OPENPRC)] <- OPEN_AVG
df_Microsoft_2018df_Microsoft_2018$text <- clean_tweets(df_Microsoft_2018$text)
df_Microsoft_2018$text <- lemmatize_words(df_Microsoft_2018$text)#Juniper_Shaygan
df_Juniper_S_2014 <- df_Juniper_S_2014[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Juniper_S_2014
#Ticker
df_Juniper_S_2014$TICKER[is.na(df_Juniper_S_2014$TICKER)] <- "JNPR"
#PRC
PRC_AVG <- mean(df_Juniper_S_2014$PRC, na.rm = TRUE)
df_Juniper_S_2014$PRC[is.na(df_Juniper_S_2014$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Juniper_S_2014$VOL, na.rm = TRUE)
df_Juniper_S_2014$VOL[is.na(df_Juniper_S_2014$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_S_2014$BID, na.rm = TRUE)
df_Juniper_S_2014$BID[is.na(df_Juniper_S_2014$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_S_2014$ASK, na.rm = TRUE)
df_Juniper_S_2014$ASK[is.na(df_Juniper_S_2014$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_S_2014$OPENPRC, na.rm = TRUE)
df_Juniper_S_2014$OPENPRC[is.na(df_Juniper_S_2014$OPENPRC)] <- OPEN_AVG
df_Juniper_S_2014df_Juniper_S_2014$text <- clean_tweets(df_Juniper_S_2014$text)
df_Juniper_S_2014$text <- lemmatize_words(df_Juniper_S_2014$text)df_Juniper_S_2016 <- df_Juniper_S_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Juniper_S_2016
#Ticker
df_Juniper_S_2016$TICKER[is.na(df_Juniper_S_2016$TICKER)] <- "JNPR"
#PRC
PRC_AVG <- mean(df_Juniper_S_2016$PRC, na.rm = TRUE)
df_Juniper_S_2016$PRC[is.na(df_Juniper_S_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Juniper_S_2016$VOL, na.rm = TRUE)
df_Juniper_S_2016$VOL[is.na(df_Juniper_S_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_S_2016$BID, na.rm = TRUE)
df_Juniper_S_2016$BID[is.na(df_Juniper_S_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_S_2016$ASK, na.rm = TRUE)
df_Juniper_S_2016$ASK[is.na(df_Juniper_S_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_S_2016$OPENPRC, na.rm = TRUE)
df_Juniper_S_2016$OPENPRC[is.na(df_Juniper_S_2016$OPENPRC)] <- OPEN_AVG
df_Juniper_S_2016df_Juniper_S_2016$text <- clean_tweets(df_Juniper_S_2016$text)
df_Juniper_S_2016$text <- lemmatize_words(df_Juniper_S_2016$text)#Synchrony Financial
df_SYFM_2016 <- df_SYFM_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_SYFM_2016
#Ticker
df_SYFM_2016$TICKER[is.na(df_SYFM_2016$TICKER)] <- "SYF"
#PRC
PRC_AVG <- mean(df_SYFM_2016$PRC, na.rm = TRUE)
df_SYFM_2016$PRC[is.na(df_SYFM_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_SYFM_2016$VOL, na.rm = TRUE)
df_SYFM_2016$VOL[is.na(df_SYFM_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_SYFM_2016$BID, na.rm = TRUE)
df_SYFM_2016$BID[is.na(df_SYFM_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_SYFM_2016$ASK, na.rm = TRUE)
df_SYFM_2016$ASK[is.na(df_SYFM_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_SYFM_2016$OPENPRC, na.rm = TRUE)
df_SYFM_2016$OPENPRC[is.na(df_SYFM_2016$OPENPRC)] <- OPEN_AVG
df_SYFM_2016
df_SYFM_2016$text <- clean_tweets(df_SYFM_2016$text)
df_SYFM_2016$text <- lemmatize_words(df_SYFM_2016$text)df_SYFM_2018 <- df_SYFM_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_SYFM_2018
#Ticker
df_SYFM_2018$TICKER[is.na(df_SYFM_2018$TICKER)] <- "SYF"
#PRC
PRC_AVG <- mean(df_SYFM_2018$PRC, na.rm = TRUE)
df_SYFM_2018$PRC[is.na(df_SYFM_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_SYFM_2018$VOL, na.rm = TRUE)
df_SYFM_2018$VOL[is.na(df_SYFM_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_SYFM_2018$BID, na.rm = TRUE)
df_SYFM_2018$BID[is.na(df_SYFM_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_SYFM_2018$ASK, na.rm = TRUE)
df_SYFM_2018$ASK[is.na(df_SYFM_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_SYFM_2018$OPENPRC, na.rm = TRUE)
df_SYFM_2018$OPENPRC[is.na(df_SYFM_2018$OPENPRC)] <- OPEN_AVG
df_SYFM_2018df_SYFM_2018$text <- clean_tweets(df_SYFM_2018$text)
df_SYFM_2018$text <- lemmatize_words(df_SYFM_2018$text)#Southern company
df_Southern_2016 <- df_Southern_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Southern_2016
#Ticker
df_Southern_2016$TICKER[is.na(df_Southern_2016$TICKER)] <- "SO"
#PRC
PRC_AVG <- mean(df_Southern_2016$PRC, na.rm = TRUE)
df_Southern_2016$PRC[is.na(df_Southern_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Southern_2016$VOL, na.rm = TRUE)
df_Southern_2016$VOL[is.na(df_Southern_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Southern_2016$BID, na.rm = TRUE)
df_Southern_2016$BID[is.na(df_Southern_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southern_2016$ASK, na.rm = TRUE)
df_Southern_2016$ASK[is.na(df_Southern_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southern_2016$OPENPRC, na.rm = TRUE)
df_Southern_2016$OPENPRC[is.na(df_Southern_2016$OPENPRC)] <- OPEN_AVG
df_Southern_2016df_Southern_2016$text <- clean_tweets(df_Southern_2016$text)
df_Southern_2016$text <- lemmatize_words(df_Southern_2016$text)df_Southern_2018 <- df_Southern_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Southern_2018
#Ticker
df_Southern_2018$TICKER[is.na(df_Southern_2018$TICKER)] <- "SO"
#PRC
PRC_AVG <- mean(df_Southern_2018$PRC, na.rm = TRUE)
df_Southern_2018$PRC[is.na(df_Southern_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Southern_2018$VOL, na.rm = TRUE)
df_Southern_2018$VOL[is.na(df_Southern_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Southern_2018$BID, na.rm = TRUE)
df_Southern_2018$BID[is.na(df_Southern_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southern_2018$ASK, na.rm = TRUE)
df_Southern_2018$ASK[is.na(df_Southern_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southern_2018$OPENPRC, na.rm = TRUE)
df_Southern_2018$OPENPRC[is.na(df_Southern_2018$OPENPRC)] <- OPEN_AVG
df_Southern_2018df_Southern_2018$text <- clean_tweets(df_Southern_2018$text)
df_Southern_2018$text <- lemmatize_words(df_Southern_2018$text)#Apple
df_Apple_2016 <- df_Apple_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Apple_2016
#Ticker
df_Apple_2016$TICKER[is.na(df_Apple_2016$TICKER)] <- "AAPL"
#PRC
PRC_AVG <- mean(df_Apple_2016$PRC, na.rm = TRUE)
df_Apple_2016$PRC[is.na(df_Apple_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Apple_2016$VOL, na.rm = TRUE)
df_Apple_2016$VOL[is.na(df_Apple_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Apple_2016$BID, na.rm = TRUE)
df_Apple_2016$BID[is.na(df_Apple_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Apple_2016$ASK, na.rm = TRUE)
df_Apple_2016$ASK[is.na(df_Apple_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Apple_2016$OPENPRC, na.rm = TRUE)
df_Apple_2016$OPENPRC[is.na(df_Apple_2016$OPENPRC)] <- OPEN_AVG
df_Apple_2016df_Apple_2016$text <- clean_tweets(df_Apple_2016$text)
df_Apple_2016$text <- lemmatize_words(df_Apple_2016$text)df_Apple_2018 <- df_Apple_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Apple_2018
#Ticker
df_Apple_2018$TICKER[is.na(df_Apple_2018$TICKER)] <- "AAPL"
#PRC
PRC_AVG <- mean(df_Apple_2018$PRC, na.rm = TRUE)
df_Apple_2018$PRC[is.na(df_Apple_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Apple_2018$VOL, na.rm = TRUE)
df_Apple_2018$VOL[is.na(df_Apple_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Apple_2018$BID, na.rm = TRUE)
df_Apple_2018$BID[is.na(df_Apple_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Apple_2018$ASK, na.rm = TRUE)
df_Apple_2018$ASK[is.na(df_Apple_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Apple_2018$OPENPRC, na.rm = TRUE)
df_Apple_2018$OPENPRC[is.na(df_Apple_2018$OPENPRC)] <- OPEN_AVG
df_Apple_2018df_Apple_2018$text <- clean_tweets(df_Apple_2018$text)
df_Apple_2018$text <- lemmatize_words(df_Apple_2018$text)#XL
df_XL_2016 <- df_XL_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_XL_2016
#Ticker
df_XL_2016$TICKER[is.na(df_XL_2016$TICKER)] <- "XL"
#PRC
PRC_AVG <- mean(df_XL_2016$PRC, na.rm = TRUE)
df_XL_2016$PRC[is.na(df_XL_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_XL_2016$VOL, na.rm = TRUE)
df_XL_2016$VOL[is.na(df_XL_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_XL_2016$BID, na.rm = TRUE)
df_XL_2016$BID[is.na(df_XL_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_XL_2016$ASK, na.rm = TRUE)
df_XL_2016$ASK[is.na(df_XL_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_XL_2016$OPENPRC, na.rm = TRUE)
df_XL_2016$OPENPRC[is.na(df_XL_2016$OPENPRC)] <- OPEN_AVG
df_XL_2016df_XL_2016$text <- clean_tweets(df_XL_2016$text)
df_XL_2016$text <- lemmatize_words(df_XL_2016$text)df_XL_2018 <- df_XL_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_XL_2018
#Ticker
df_XL_2018$TICKER[is.na(df_XL_2018$TICKER)] <- "XL"
#PRC
PRC_AVG <- mean(df_XL_2018$PRC, na.rm = TRUE)
df_XL_2018$PRC[is.na(df_XL_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_XL_2018$VOL, na.rm = TRUE)
df_XL_2018$VOL[is.na(df_XL_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_XL_2018$BID, na.rm = TRUE)
df_XL_2018$BID[is.na(df_XL_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_XL_2018$ASK, na.rm = TRUE)
df_XL_2018$ASK[is.na(df_XL_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_XL_2018$OPENPRC, na.rm = TRUE)
df_XL_2018$OPENPRC[is.na(df_XL_2018$OPENPRC)] <- OPEN_AVG
df_XL_2018df_XL_2018$text <- clean_tweets(df_XL_2018$text)
df_XL_2018$text <- lemmatize_words(df_XL_2018$text)#Tyson foods
df_Tysonf_2016 <- df_Tysonf_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Tysonf_2016
#Ticker
df_Tysonf_2016$TICKER[is.na(df_Tysonf_2016$TICKER)] <- "TSN"
#PRC
PRC_AVG <- mean(df_Tysonf_2016$PRC, na.rm = TRUE)
df_Tysonf_2016$PRC[is.na(df_Tysonf_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Tysonf_2016$VOL, na.rm = TRUE)
df_Tysonf_2016$VOL[is.na(df_Tysonf_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Tysonf_2016$BID, na.rm = TRUE)
df_Tysonf_2016$BID[is.na(df_Tysonf_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Tysonf_2016$ASK, na.rm = TRUE)
df_Tysonf_2016$ASK[is.na(df_Tysonf_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Tysonf_2016$OPENPRC, na.rm = TRUE)
df_Tysonf_2016$OPENPRC[is.na(df_Tysonf_2016$OPENPRC)] <- OPEN_AVG
df_Tysonf_2016df_Tysonf_2016$text <- clean_tweets(df_Tysonf_2016$text)
df_Tysonf_2016$text <- lemmatize_words(df_Tysonf_2016$text)#Tyson foods
df_Tysonf_2018 <- df_Tysonf_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Tysonf_2018
#Ticker
df_Tysonf_2018$TICKER[is.na(df_Tysonf_2018$TICKER)] <- "TSN"
#PRC
PRC_AVG <- mean(df_Tysonf_2018$PRC, na.rm = TRUE)
df_Tysonf_2018$PRC[is.na(df_Tysonf_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Tysonf_2018$VOL, na.rm = TRUE)
df_Tysonf_2018$VOL[is.na(df_Tysonf_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Tysonf_2018$BID, na.rm = TRUE)
df_Tysonf_2018$BID[is.na(df_Tysonf_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Tysonf_2018$ASK, na.rm = TRUE)
df_Tysonf_2018$ASK[is.na(df_Tysonf_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Tysonf_2018$OPENPRC, na.rm = TRUE)
df_Tysonf_2018$OPENPRC[is.na(df_Tysonf_2018$OPENPRC)] <- OPEN_AVG
df_Tysonf_2018df_Tysonf_2018$text <- clean_tweets(df_Tysonf_2018$text)
df_Tysonf_2018$text <- lemmatize_words(df_Tysonf_2018$text)#Akamai
df_Akamai_2016 <- df_Akamai_2016[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Akamai_2016
#Ticker
df_Akamai_2016$TICKER[is.na(df_Akamai_2016$TICKER)] <- "AKAM"
#PRC
PRC_AVG <- mean(df_Akamai_2016$PRC, na.rm = TRUE)
df_Akamai_2016$PRC[is.na(df_Akamai_2016$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Akamai_2016$VOL, na.rm = TRUE)
df_Akamai_2016$VOL[is.na(df_Akamai_2016$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Akamai_2016$BID, na.rm = TRUE)
df_Akamai_2016$BID[is.na(df_Akamai_2016$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Akamai_2016$ASK, na.rm = TRUE)
df_Akamai_2016$ASK[is.na(df_Akamai_2016$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Akamai_2016$OPENPRC, na.rm = TRUE)
df_Akamai_2016$OPENPRC[is.na(df_Akamai_2016$OPENPRC)] <- OPEN_AVG
df_Akamai_2016
df_Akamai_2016$text <- clean_tweets(df_Akamai_2016$text)
df_Akamai_2016$text <- lemmatize_words(df_Akamai_2016$text)df_Akamai_2018 <- df_Akamai_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_Akamai_2018
#Ticker
df_Akamai_2018$TICKER[is.na(df_Akamai_2018$TICKER)] <- "AKAM"
#PRC
PRC_AVG <- mean(df_Akamai_2018$PRC, na.rm = TRUE)
df_Akamai_2018$PRC[is.na(df_Akamai_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_Akamai_2018$VOL, na.rm = TRUE)
df_Akamai_2018$VOL[is.na(df_Akamai_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_Akamai_2018$BID, na.rm = TRUE)
df_Akamai_2018$BID[is.na(df_Akamai_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_Akamai_2018$ASK, na.rm = TRUE)
df_Akamai_2018$ASK[is.na(df_Akamai_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Akamai_2018$OPENPRC, na.rm = TRUE)
df_Akamai_2018$OPENPRC[is.na(df_Akamai_2018$OPENPRC)] <- OPEN_AVG
df_Akamai_2018df_Akamai_2018$text <- clean_tweets(df_Akamai_2018$text)
df_Akamai_2018$text <- lemmatize_words(df_Akamai_2018$text)#Google
df_GOOGL_2015 <-merge(GOOGL_2015, GOOGL_SP_2016, by = "date", all.x = TRUE)
write.csv(df_GOOGL_2015, file = "Google_2015", row.names = FALSE)
df_GOOGL_2015 <- df_GOOGL_2015[,-c(6,8,9,10,15,17,18,19,20,21)]
df_GOOGL_2015
#Ticker
df_GOOGL_2015$TICKER[is.na(df_GOOGL_2015$TICKER)] <- "GOOGL"
#PRC
PRC_AVG <- mean(df_GOOGL_2015$PRC, na.rm = TRUE)
df_GOOGL_2015$PRC[is.na(df_GOOGL_2015$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_GOOGL_2015$VOL, na.rm = TRUE)
df_GOOGL_2015$VOL[is.na(df_GOOGL_2015$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_GOOGL_2015$BID, na.rm = TRUE)
df_GOOGL_2015$BID[is.na(df_GOOGL_2015$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_GOOGL_2015$ASK, na.rm = TRUE)
df_GOOGL_2015$ASK[is.na(df_GOOGL_2015$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GOOGL_2015$OPENPRC, na.rm = TRUE)
df_GOOGL_2015$OPENPRC[is.na(df_GOOGL_2015$OPENPRC)] <- OPEN_AVG
df_GOOGL_2015df_GOOGL_2015$text <- clean_tweets(df_GOOGL_2015$text)
df_GOOGL_2015$text <- lemmatize_words(df_GOOGL_2015$text)df_GOOGL_2018 <-merge(GOOGL_2018, GOOGLE_SP_2018, by = "date", all.x = TRUE)
write.csv(df_GOOGL_2018, file = "Google_2018", row.names = FALSE)
df_GOOGL_2018 <- df_GOOGL_2018[,-c(6,8,9,10,15,17,18,19,20,21)]
df_GOOGL_2018
#Ticker
df_GOOGL_2018$TICKER[is.na(df_GOOGL_2018$TICKER)] <- "GOOGL"
#PRC
PRC_AVG <- mean(df_GOOGL_2018$PRC, na.rm = TRUE)
df_GOOGL_2018$PRC[is.na(df_GOOGL_2018$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_GOOGL_2018$VOL, na.rm = TRUE)
df_GOOGL_2018$VOL[is.na(df_GOOGL_2018$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_GOOGL_2018$BID, na.rm = TRUE)
df_GOOGL_2018$BID[is.na(df_GOOGL_2018$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_GOOGL_2018$ASK, na.rm = TRUE)
df_GOOGL_2018$ASK[is.na(df_GOOGL_2018$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GOOGL_2018$OPENPRC, na.rm = TRUE)
df_GOOGL_2018$OPENPRC[is.na(df_GOOGL_2018$OPENPRC)] <- OPEN_AVG
df_GOOGL_2018df_GOOGL_2018$text <- clean_tweets(df_GOOGL_2018$text)
df_GOOGL_2018$text <- lemmatize_words(df_GOOGL_2018$text)##Covid datasets
#WU
df_WU_covid <-merge(WU_covid, WU_SP_covid, by = "date", all.x = TRUE)
write.csv(df_WU_covid, file = "WU_COVID", row.names = FALSE)
df_WU_covid <- df_WU_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_WU_covid
#Ticker
df_WU_covid$TICKER[is.na(df_WU_covid$TICKER)] <- "WU"
#PRC
PRC_AVG <- mean(df_WU_covid$PRC, na.rm = TRUE)
df_WU_covid$PRC[is.na(df_WU_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_WU_covid$VOL, na.rm = TRUE)
df_WU_covid$VOL[is.na(df_WU_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_WU_covid$BID, na.rm = TRUE)
df_WU_covid$BID[is.na(df_WU_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_WU_covid$ASK, na.rm = TRUE)
df_WU_covid$ASK[is.na(df_WU_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WU_covid$OPENPRC, na.rm = TRUE)
df_WU_covid$OPENPRC[is.na(df_WU_covid$OPENPRC)] <- OPEN_AVG
df_WU_coviddf_WU_covid$text <- clean_tweets(df_WU_covid$text)
df_WU_covid$text <- lemmatize_words(df_WU_covid$text)#AAPL
df_AAPL_covid <-merge(AAPL_covid, AAPL_SP_covid, by = "date", all.x = TRUE)
write.csv(df_AAPL_covid, file = "AAPL_COVID", row.names = FALSE)
df_AAPL_covid <- df_AAPL_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_AAPL_covid
#Ticker
df_AAPL_covid$TICKER[is.na(df_AAPL_covid$TICKER)] <- "AAPL"
#PRC
PRC_AVG <- mean(df_AAPL_covid$PRC, na.rm = TRUE)
df_AAPL_covid$PRC[is.na(df_AAPL_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_AAPL_covid$VOL, na.rm = TRUE)
df_AAPL_covid$VOL[is.na(df_AAPL_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_AAPL_covid$BID, na.rm = TRUE)
df_AAPL_covid$BID[is.na(df_AAPL_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_AAPL_covid$ASK, na.rm = TRUE)
df_AAPL_covid$ASK[is.na(df_AAPL_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AAPL_covid$OPENPRC, na.rm = TRUE)
df_AAPL_covid$OPENPRC[is.na(df_AAPL_covid$OPENPRC)] <- OPEN_AVG
df_AAPL_coviddf_AAPL_covid$text <- clean_tweets(df_AAPL_covid$text)
df_AAPL_covid$text <- lemmatize_words(df_AAPL_covid$text)#RMD
df_RMD_covid <-merge(RMD_covid, RMD_SP_covid, by = "date", all.x = TRUE)
write.csv(df_RMD_covid, file = "RMD_COVID", row.names = FALSE)
df_RMD_covid <- df_RMD_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_RMD_covid
#Ticker
df_RMD_covid$TICKER[is.na(df_RMD_covid$TICKER)] <- "RMD"
#PRC
PRC_AVG <- mean(df_RMD_covid$PRC, na.rm = TRUE)
df_RMD_covid$PRC[is.na(df_RMD_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_RMD_covid$VOL, na.rm = TRUE)
df_RMD_covid$VOL[is.na(df_RMD_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_RMD_covid$BID, na.rm = TRUE)
df_RMD_covid$BID[is.na(df_RMD_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_RMD_covid$ASK, na.rm = TRUE)
df_RMD_covid$ASK[is.na(df_RMD_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RMD_covid$OPENPRC, na.rm = TRUE)
df_RMD_covid$OPENPRC[is.na(df_RMD_covid$OPENPRC)] <- OPEN_AVG
df_RMD_coviddf_RMD_covid$text <- clean_tweets(df_RMD_covid$text)
df_RMD_covid$text <- lemmatize_words(df_RMD_covid$text)#GOOGL
df_GOOGL_covid <-merge(GOOGL_covid, GOOGL_SP_covid, by = "date", all.x = TRUE)
write.csv(df_GOOGL_covid, file = "GOOGL_COVID", row.names = FALSE)
df_GOOGL_covid <- df_GOOGL_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_GOOGL_covid
#Ticker
df_GOOGL_covid$TICKER[is.na(df_GOOGL_covid$TICKER)] <- "GOOGL"
#PRC
PRC_AVG <- mean(df_GOOGL_covid$PRC, na.rm = TRUE)
df_GOOGL_covid$PRC[is.na(df_GOOGL_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_GOOGL_covid$VOL, na.rm = TRUE)
df_GOOGL_covid$VOL[is.na(df_GOOGL_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_GOOGL_covid$BID, na.rm = TRUE)
df_GOOGL_covid$BID[is.na(df_GOOGL_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_GOOGL_covid$ASK, na.rm = TRUE)
df_GOOGL_covid$ASK[is.na(df_GOOGL_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GOOGL_covid$OPENPRC, na.rm = TRUE)
df_GOOGL_covid$OPENPRC[is.na(df_GOOGL_covid$OPENPRC)] <- OPEN_AVG
df_GOOGL_coviddf_GOOGL_covid$text <- clean_tweets(df_GOOGL_covid$text)
df_GOOGL_covid$text <- lemmatize_words(df_GOOGL_covid$text)#AGN
df_AGN_covid <-merge(AGN_covid, AGN_SP_covid, by = "date", all.x = TRUE)
write.csv(df_AGN_covid, file = "AGN_COVID", row.names = FALSE)
df_AGN_covid <- df_AGN_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_AGN_covid
#Ticker
df_AGN_covid$TICKER[is.na(df_AGN_covid$TICKER)] <- "AGN"
#PRC
PRC_AVG <- mean(df_AGN_covid$PRC, na.rm = TRUE)
df_AGN_covid$PRC[is.na(df_AGN_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_AGN_covid$VOL, na.rm = TRUE)
df_AGN_covid$VOL[is.na(df_AGN_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_AGN_covid$BID, na.rm = TRUE)
df_AGN_covid$BID[is.na(df_AGN_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_AGN_covid$ASK, na.rm = TRUE)
df_AGN_covid$ASK[is.na(df_AGN_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AGN_covid$OPENPRC, na.rm = TRUE)
df_AGN_covid$OPENPRC[is.na(df_AGN_covid$OPENPRC)] <- OPEN_AVG
df_AGN_coviddf_AGN_covid$text <- clean_tweets(df_AGN_covid$text)
df_AGN_covid$text <- lemmatize_words(df_AGN_covid$text)#AMAZON
df_AMZN_covid <-merge(AMZN_covid, AMZN_SP_covid, by = "date", all.x = TRUE)
write.csv(df_AMZN_covid, file = "AMZN_COVID", row.names = FALSE)
df_AMZN_covid <- df_AMZN_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_AMZN_covid
#Ticker
df_AMZN_covid$TICKER[is.na(df_AMZN_covid$TICKER)] <- "AMZN"
#PRC
PRC_AVG <- mean(df_AMZN_covid$PRC, na.rm = TRUE)
df_AMZN_covid$PRC[is.na(df_AMZN_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_AMZN_covid$VOL, na.rm = TRUE)
df_AMZN_covid$VOL[is.na(df_AMZN_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_AMZN_covid$BID, na.rm = TRUE)
df_AMZN_covid$BID[is.na(df_AMZN_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMZN_covid$ASK, na.rm = TRUE)
df_AMZN_covid$ASK[is.na(df_AMZN_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMZN_covid$OPENPRC, na.rm = TRUE)
df_AMZN_covid$OPENPRC[is.na(df_AMZN_covid$OPENPRC)] <- OPEN_AVG
df_AMZN_covid
df_AMZN_covid$text <- clean_tweets(df_AMZN_covid$text)
df_AMZN_covid$text <- lemmatize_words(df_AMZN_covid$text)#Disney
df_DIS_covid <-merge(DIS_covid, DIS_SP_covid, by = "date", all.x = TRUE)
write.csv(df_DIS_covid, file = "DIS_COVID", row.names = FALSE)
df_DIS_covid <- df_DIS_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_DIS_covid
#Ticker
df_DIS_covid$TICKER[is.na(df_DIS_covid$TICKER)] <- "DIS"
#PRC
PRC_AVG <- mean(df_DIS_covid$PRC, na.rm = TRUE)
df_DIS_covid$PRC[is.na(df_DIS_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_DIS_covid$VOL, na.rm = TRUE)
df_DIS_covid$VOL[is.na(df_DIS_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_DIS_covid$BID, na.rm = TRUE)
df_DIS_covid$BID[is.na(df_DIS_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_DIS_covid$ASK, na.rm = TRUE)
df_DIS_covid$ASK[is.na(df_DIS_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_DIS_covid$OPENPRC, na.rm = TRUE)
df_DIS_covid$OPENPRC[is.na(df_DIS_covid$OPENPRC)] <- OPEN_AVG
df_DIS_covid
df_DIS_covid$text <- clean_tweets(df_DIS_covid$text)
df_DIS_covid$text <- lemmatize_words(df_DIS_covid$text)#Microsoft
df_MSFT_covid <-merge(MSFT_covid, MSFT_SP_covid, by = "date", all.x = TRUE)
write.csv(df_MSFT_covid, file = "MSFT_COVID", row.names = FALSE)
df_MSFT_covid <- df_MSFT_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_MSFT_covid
#Ticker
df_MSFT_covid$TICKER[is.na(df_MSFT_covid$TICKER)] <- "MSFT"
#PRC
PRC_AVG <- mean(df_MSFT_covid$PRC, na.rm = TRUE)
df_MSFT_covid$PRC[is.na(df_MSFT_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_MSFT_covid$VOL, na.rm = TRUE)
df_MSFT_covid$VOL[is.na(df_MSFT_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_MSFT_covid$BID, na.rm = TRUE)
df_MSFT_covid$BID[is.na(df_MSFT_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_MSFT_covid$ASK, na.rm = TRUE)
df_MSFT_covid$ASK[is.na(df_MSFT_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_MSFT_covid$OPENPRC, na.rm = TRUE)
df_MSFT_covid$OPENPRC[is.na(df_MSFT_covid$OPENPRC)] <- OPEN_AVG
df_MSFT_coviddf_MSFT_covid$text <- clean_tweets(df_MSFT_covid$text)
df_MSFT_covid$text <- lemmatize_words(df_MSFT_covid$text)#Medtronic
df_MDT_covid <-merge(MDT_Covid, MDT_SP_covid, by = "date", all.x = TRUE)
write.csv(df_MDT_covid, file = "MDT_COVID", row.names = FALSE)
df_MDT_covid <- df_MDT_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_MDT_covid
#Ticker
df_MDT_covid$TICKER[is.na(df_MDT_covid$TICKER)] <- "MDT"
#PRC
PRC_AVG <- mean(df_MDT_covid$PRC, na.rm = TRUE)
df_MDT_covid$PRC[is.na(df_MDT_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_MDT_covid$VOL, na.rm = TRUE)
df_MDT_covid$VOL[is.na(df_MDT_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_MDT_covid$BID, na.rm = TRUE)
df_MDT_covid$BID[is.na(df_MDT_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_MDT_covid$ASK, na.rm = TRUE)
df_MDT_covid$ASK[is.na(df_MDT_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_MDT_covid$OPENPRC, na.rm = TRUE)
df_MDT_covid$OPENPRC[is.na(df_MDT_covid$OPENPRC)] <- OPEN_AVG
df_MDT_coviddf_MDT_covid$text <- clean_tweets(df_MDT_covid$text)
df_MDT_covid$text <- lemmatize_words(df_MDT_covid$text)#RedHat
df_RHT_covid <-merge(RHT_covid, RHT_SP_covid, by = "date", all.x = TRUE)
write.csv(df_RHT_covid, file = "RHT_COVID", row.names = FALSE)
df_RHT_covid <- df_RHT_covid[,-c(6,8,9,10,15,17,18,19,20,21)]
df_RHT_covid
#Ticker
df_RHT_covid$TICKER[is.na(df_RHT_covid$TICKER)] <- "RHT"
#PRC
PRC_AVG <- mean(df_RHT_covid$PRC, na.rm = TRUE)
df_RHT_covid$PRC[is.na(df_RHT_covid$PRC)] <- PRC_AVG
#VOL
VOL_AVG <- mean(df_RHT_covid$VOL, na.rm = TRUE)
df_RHT_covid$VOL[is.na(df_RHT_covid$VOL)] <- VOL_AVG
#BID
BID_AVG <- BID_AVG <- mean(df_RHT_covid$BID, na.rm = TRUE)
df_RHT_covid$BID[is.na(df_RHT_covid$BID)] <- BID_AVG
#ASK
ASK_AVG <- ASK_AVG <- mean(df_RHT_covid$ASK, na.rm = TRUE)
df_RHT_covid$ASK[is.na(df_RHT_covid$ASK)] <- ASK_AVG
#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RHT_covid$OPENPRC, na.rm = TRUE)
df_RHT_covid$OPENPRC[is.na(df_RHT_covid$OPENPRC)] <- OPEN_AVG
df_RHT_coviddf_RHT_covid$text <- clean_tweets(df_RHT_covid$text)
df_RHT_covid$text <- lemmatize_words(df_RHT_covid$text)##Wordclouds
#Packages
library(wordcloud)Loading required package: RColorBrewerlibrary(RColorBrewer)
library(wordcloud2)Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudiolibrary(tm)Loading required package: NLPlibrary("tidyverse")Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ───────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.4.2 ✔ purrr 0.3.4
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.1 ✔ stringr 1.5.0
✔ readr 2.1.4 ✔ forcats 0.5.2 ── Conflicts ──────────────────────────────────── tidyverse_conflicts() ──
✖ ggplot2::annotate() masks NLP::annotate()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()#vector containing only the text
text <- df_Test$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))
dtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))#Johnson_2016
#vector containing only the text
text <- df_Johnson_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Johnson 2017
#vector containing only the text
text <- df_Johnson_2017$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Nasdaq
#vector containing only the text
text <- df_NASDAQ$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Autodesk
#vector containing only the text
text <- df_autodesk$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Hasbro
#vector containing only the text
text <- df_Hasbro_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#vector containing only the text
text <- df_Hasbro_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Intel 2016
#vector containing only the text
text <- df_Intel_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Intel 2018
#vector containing only the text
text <- df_Intel_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Activision 2016
#vector containing only the text
text <- df_Activision_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Activision 2016
#vector containing only the text
text <- df_Activision_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Inuit 2016
#vector containing only the text
text <- df_Inuit_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Inuit 2018
#vector containing only the text
text <- df_Inuit_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Allergan 2016
#vector containing only the text
text <- df_Allergan_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Allergan 2018
#vector containing only the text
text <- df_Allergan_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Humana 2016
#vector containing only the text
text <- df_Humana_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Humana 2018
#vector containing only the text
text <- df_Humana_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Autodesk CB 2016
#vector containing only the text
text <- df_CB_autodesk_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Autodesk 2018
#vector containing only the text
text <- df_CB_autodesk_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Equinox 2018
#vector containing only the text
text <- df_equinox_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Cisco 2016
#vector containing only the text
text <- df_cisco_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Cisco 2018
#vector containing only the text
text <- df_cisco_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#EBAY 2016
#vector containing only the text
text <- df_EBAY_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#vector containing only the text
text <- df_EBAY_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Davita 2016
#vector containing only the text
text <- df_Davita_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Davita 2018
#vector containing only the text
text <- df_Davita_2017$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Illumina 2016
#vector containing only the text
text <- df_Illumina_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Illumina 2018
#vector containing only the text
text <- df_Illumina_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Homedepot
#vector containing only the text
text <- df_Homedepot_2013$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Southwest airlines 2016
#vector containing only the text
text <- df_Southwest_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Southwest Airlines 2018
#vector containing only the text
text <- df_Southwest_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#FIS 2016
#vector containing only the text
text <- df_FIS_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#FIS 2018
#vector containing only the text
text <- df_FIS_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Leucadia Nation
#vector containing only the text
text <- df_Leucadia_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Leucadia Nation 2018
#vector containing only the text
text <- df_Leucadia_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Verizon
#vector containing only the text
text <- df_Verizon_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
text <- df_WU_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Western Union 2018
#vector containing only the text
text <- df_WU_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#RedHat 2016
#vector containing only the text
text <- df_RedHat_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#RedHat 2018
#vector containing only the text
text <- df_RedHat_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Amazon 2016
#vector containing only the text
text <- df_AMZN_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
text <- df_AMZN_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#vector containing only the text
text <- df_GE_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#GE 2017
#vector containing only the text
text <- df_GE_2017$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Fiserv 2016
#vector containing only the text
text <- df_Fiserv_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Fiserv 2018
#vector containing only the text
text <- df_Fiserv_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#vector containing only the text
text <- df_WM_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Waste Management 2018
#vector containing only the text
text <- df_WM_2018$text#Wills Tower 2016
#vector containing only the text
text <- df_Wills_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Wills Tower 2018
#vector containing only the text
text <- df_Wills_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Tripadvisor 2016
#vector containing only the text
text <- df_tripadvisor_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiRXJyb3I6IGF0dGVtcHQgdG8gdXNlIHplcm8tbGVuZ3RoIHZhcmlhYmxlIG5hbWVcbiJ9 -->
Error: attempt to use zero-length variable name
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI1RyaXBhZHZpc29yIDIwMThcblxuI3ZlY3RvciBjb250YWluaW5nIG9ubHkgdGhlIHRleHRcbnRleHQgPC0gZGZfdHJpcGFkdmlzb3JfMjAxOCR0ZXh0XG4jY29ycHVzICBcbmRvY3MgPC0gQ29ycHVzKFZlY3RvclNvdXJjZSh0ZXh0KSlcblxuZG9jcyA8LSBkb2NzICU+JVxuICB0bV9tYXAocmVtb3ZlTnVtYmVycykgJT4lXG4gIHRtX21hcChyZW1vdmVQdW5jdHVhdGlvbikgJT4lXG4gIHRtX21hcChzdHJpcFdoaXRlc3BhY2UpXG5gYGAifQ== -->
```r
#Tripadvisor 2018
#vector containing only the text
text <- df_tripadvisor_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#DavitaKent
#vector containing only the text
text <- df_DavitaK_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Davita Kent 2018
#vector containing only the text
text <- df_DavitaK_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Starbucks 2018
#vector containing only the text
text <- df_Starbucks_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#McCormick 2018
#vector containing only the text
text <- df_McCormick_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#IHS Markit 2018
#vector containing only the text
text <- df_IHS_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#vector containing only the text
text <- df_AMD_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#ResMed 2016
#vector containing only the text
text <- df_ResMed_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#ResMed 2018
#vector containing only the text
text <- df_ResMed_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#CA 2016
#vector containing only the text
text <- df_CA_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#CA 2018
#vector containing only the text
text <- df_CA_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#GM 2016
#vector containing only the text
text <- df_GM_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#GM 2018
#vector containing only the text
text <- df_GM_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Aetna 2016
#vector containing only the text
text <- df_Aetna_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Aetna 2018
#vector containing only the text
text <- df_Aetna_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#NRG 2016
#vector containing only the text
text <- df_NRG_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#NRG 2018
#NRG 2016
#vector containing only the text
text <- df_NRG_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Medtronic 2016
#vector containing only the text
text <- df_Medtronic_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Medtronic 2018
#vector containing only the text
text <- df_Medtronic_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Juniper 2016
#vector containing only the text
text <- df_Juniper_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Juniper 2018
#vector containing only the text
text <- df_Juniper_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Aetna Ramon
#vector containing only the text
text <- df_AetnaR_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Netflix
#vector containing only the text
text <- df_Netflix_2016$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Disney
#vector containing only the text
text <- df_Disney_2018$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
#Fox 2015
#vector containing only the text
text <- df_Fox_2015$text
#corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df_wc <- data.frame(word = names(words),freq=words)
wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*.

```{r}
plot(cars)
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```{r}
setwd("/Users/jayshreenohar/Downloads")
```

##Merge datasets

```{r}
#Johnson
#2016
# Merge dataframes
df_Johnson_2016 <- merge(Johnson_2016_Tweets_CEO, Johnson_sp_2016, by ="date", all.x = TRUE)
write.csv(df_Johnson_2016, file = "Johnson_df_2016", row.names = FALSE)

#2017
df_Johnson_2017 <- merge(Tweets_2017_Johnson, Johnson_sp_2017, by ="date", all.x = TRUE)
write.csv(df_Johnson_2017, file = "Johnson_df_2017", row.names = FALSE)

df_Johnson_2016
```

```{r}
df_Johnson_2017
```

```{r}
#Nasdaq
#2017-2018
df_NASDAQ <- merge(Tweets_Nasdaq, NASDAQ_new, by = "date", all.x = TRUE)
write.csv(df_NASDAQ, file = "Nasdaq_2017", row.names = FALSE)

df_NASDAQ
```

```{r}
#Autodesk
#2018
df_autodesk <- merge(Autodesk_Tweets, Autodesk_SP_2018, by = "date", all.x = TRUE)
write.csv(df_autodesk, file = "autodesk_2018", row.names = FALSE)

df_autodesk
```

```{r}
#Hasbro
#2018
df_Hasbro_2018 <- merge(Hasbro_Tweets_2018, Hasbro_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Hasbro_2018, file = "Hasbro_2018", row.names = FALSE)

#2016
df_Hasbro_2016 <- merge(Hasbro_Tweets_2016, Hasbro_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Hasbro_2016, file = "Hasbro_2018", row.names = FALSE)

df_Hasbro_2016
```

```{r}
df_Hasbro_2018
```

```{r}
#Intel
#2018
df_Intel_2018 <- merge(Intel_Tweets_2018, Intel_2018_SP, by = "date", all.x = TRUE)
write.csv(df_Intel_2018, file = "Intel_2018", row.names = FALSE)

#2016
df_Intel_2016 <- merge(Intel_Tweets_2016, Intel_2016, by = "date", all.x = TRUE)
write.csv(df_Intel_2016, file = "Intel_2016", row.names = FALSE)

df_Intel_2016
```

```{r}
df_Intel_2018
```

```{r}
#Activision
#2016
df_Activision_2016 <- merge(Activision_Tweets_2016, Activision_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Activision_2016, file = "Activision_2016", row.names = FALSE)

#2018
df_Activision_2018 <- merge(Activision_Tweets_2018, Activision_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Activision_2018, file = "Activision_2018", row.names = FALSE)

df_Activision_2016
```

```{r}
df_Activision_2018
```

```{r}
#Inuit 
#2016
df_Inuit_2016 <- merge(Tweets_Inuit_2016, Inuit_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Inuit_2016, file = "Intuit_2016", row.names = FALSE)

#2018
df_Inuit_2018 <- merge(Tweets_Inuit_2018, Intuit_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Inuit_2018, file = "Intuit_2018", row.names = FALSE)

df_Inuit_2016
```

```{r}
df_Inuit_2018
```

```{r}
#Allergan 
#2016
df_Allergan_2016 <- merge(Allergan_Tweets_2016, Allergan_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Allergan_2016, file = "Allergan_2016", row.names = FALSE)

#2018
df_Allergan_2018 <- merge(Allergan_Tweets_2018, Allergan_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Allergan_2018, file = "Allergan_2018", row.names = FALSE)

df_Allergan_2016
```

```{r}
df_Allergan_2018
```

```{r}
#Humana
#2016
df_Humana_2016 <- merge(Humana_Tweets_2016, Humana_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Humana_2016, file = "Humana_2016", row.names = FALSE)

#2018
df_Humana_2018 <- merge(Humana_2018_Tweets, Humana_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Humana_2018, file = "Humana_2018", row.names = FALSE)

df_Humana_2016
```

```{r}
df_Humana_2018
```

```{r}
#Autodesk_Carl_Bass
#2016
df_CB_autodesk_2016 <- merge(Autodesk_CB_tweets_2016, Autodesk_CB_SP_2016, by = "date", all.x = TRUE)
write.csv(df_CB_autodesk_2016, file = "Autodesk_CB_2016", row.names = FALSE)

#2018
df_CB_autodesk_2018 <- merge(Autodesk_CB_tweets_2017, Autodesk_2017_SP_CB, by = "date", all.x = TRUE)
write.csv(df_CB_autodesk_2018, file = "Autodesk_CB_2018", row.names = FALSE)

df_CB_autodesk_2016
```

```{r}
df_CB_autodesk_2018
```

```{r}
#Equinox
#2018
df_equinox_2018 <- merge(Equinox_2018_Tweets, Equinox_2018_SP, by = "date", all.x = TRUE)
write.csv(df_equinox_2018, file = "Equinox_2018", row.names = FALSE)
df_equinox_2018
```

```{r}
#Cisco
#2016
df_cisco_2016 <- merge(Cisco_Tweets_2016, Cisco_SP_2016, by = "date", all.x = TRUE)
write.csv(df_cisco_2016, file = "Cisco_2016", row.names = FALSE)

#2018
df_cisco_2018 <- merge(Cisco_Tweets_2018, Cisco_SP_2018, by = "date", all.x = TRUE)
write.csv(df_cisco_2018, file = "Cisco_2018", row.names = FALSE)

df_cisco_2016
```

```{r}
df_cisco_2018
```

```{r}
#EBAY
#2016
df_EBAY_2016 <-merge(EBAY_Tweets_2016, EBAY_2016_SP, by = "date", all.x = TRUE)
write.csv(df_EBAY_2016, file = "EBAY_2016", row.names = FALSE)

#2018
df_EBAY_2018 <-merge(EBAY_Tweets_2018, EBAY_SP_2018, by = "date", all.x = TRUE)
write.csv(df_EBAY_2018, file = "EBAY_2018", row.names = FALSE)

df_EBAY_2016
```

```{r}
df_EBAY_2018
```

```{r}
#Davita
#2016
df_Davita_2016 <-merge(Tweets_Davita_2016, Davita_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Davita_2016, file = "Davita_2016", row.names = FALSE)

#2017
df_Davita_2017 <-merge(Davita_Inc_Tweets_2017, Davita_SP_2017, by = "date", all.x = TRUE)
write.csv(df_Davita_2017, file = "Davita_2017", row.names = FALSE)

df_Davita_2016
```

```{r}
df_Davita_2017
```

```{r}
#Illumina 
#2016
df_Illumina_2016 <-merge(Illumina_Tweets_2016, Illumina_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Illumina_2016, file = "Illumina_2016", row.names = FALSE)

#2018
df_Illumina_2018 <-merge(Illumina_Tweets_2017_2018, Illumina_2017_2018_SP, by = "date", all.x = TRUE)
write.csv(df_Illumina_2018, file = "Illumina_2018", row.names = FALSE)

df_Illumina_2016
```

```{r}
df_Illumina_2018
```

```{r}
#HomeDepot
#2013-2014
df_Homedepot_2013 <-merge(Home_depot_tweets_2013,Home_Depot_SP, by = "date", all.x = TRUE)
write.csv(df_Homedepot_2013, file = "Homedepot_2013", row.names = TRUE)
df_Homedepot_2013
```

```{r}
#Southwest_Airlines
#2016
df_Southwest_2016 <-merge(Southwest_Tweets_2016,Southwest_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Southwest_2016, file = "Southwest_2016", row.names = FALSE)


#2018
df_Southwest_2018 <-merge(Southwest_Tweets_2018,Southwest_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Southwest_2018, file = "Southwest_2018", row.names = FALSE)
df_Southwest_2016
```

```{r}
df_Southwest_2018
```

```{r}
#FIS
#2016
df_FIS_2016 <-merge(FIS_Tweets_2016, FIS_2016_SP, by = "date", all.x = TRUE)
write.csv(df_FIS_2016, file = "FIS_2016", row.names = FALSE)

#2018
df_FIS_2018 <-merge(FIS_Tweets_2018, FIS_2018_SP, by = "date", all.x = TRUE)
write.csv(df_FIS_2018, file = "FIS_2018", row.names = FALSE)
df_FIS_2016
```

```{r}
df_FIS_2018
```

```{r}
#Leucadia_Nation
#2016
df_Leucadia_2016 <-merge(Leucadia_Tweets_2016, Leucadia_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Leucadia_2016, file = "Leucadia_2016", row.names = FALSE)

#2018
df_Leucadia_2018 <-merge(Leucadia_2018_Tweets, Leucadia_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Leucadia_2018, file = "Leucadia_2018", row.names = FALSE)

df_Leucadia_2016
```

```{r}
df_Leucadia_2018
```

```{r}
#Verizon
#2018
df_Verizon_2018 <- merge(Verizon_Tweets_2018, Verizon_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Verizon_2018, file = "Verizon_2018", row.names = FALSE)

df_Verizon_2018
```

```{r}
#Western Union 
#2018
df_WU_2018 <- merge(Western_Union_Tweets_2018, WU_SP_2018, by = "date", all.x = TRUE)
write.csv(df_WU_2018, file = "WU_2018", row.names = FALSE)

#2016
df_WU_2016 <- merge(Western_Union_Tweets_2016, WU_SP_2016, by = "date", all.x = TRUE)
write.csv(df_WU_2016, file = "WU_2016", row.names = FALSE)

df_WU_2016
```

```{r}
df_WU_2018
```

```{r}
#Red_Hat
#2016
df_RedHat_2016 <- merge(Red_Hat_Tweets_2016, Red_Hat_SP_2016, by = "date", all.x = TRUE)
write.csv(df_RedHat_2016, file = "RedHat_2016", row.names = FALSE)

#2018
df_RedHat_2018 <- merge(Red_Hat_Tweets_2018, Red_Hat_SP_2018, by = "date", all.x = TRUE)
write.csv(df_RedHat_2018, file = "RedHat_2018", row.names = FALSE)

df_RedHat_2016
```

```{r}
df_RedHat_2018
```

```{r}
#AMZN
#2016
df_AMZN_2016 <- merge(AMZN_Tweets_2016, AMZN_SP_2016, by = "date", all.x = TRUE)
write.csv(df_AMZN_2016, file = "AMZN_2016", row.names = FALSE)

#2018
df_AMZN_2018 <- merge(AMZN_Tweets_2018, AMZN_SP_2018, by = "date", all.x = TRUE)
write.csv(df_AMZN_2018, file = "AMZN_2018", row.names = FALSE)

df_AMZN_2016
```

```{r}
df_AMZN_2018
```

```{r}
#GE
#2016
df_GE_2016 <- merge(GE_Tweets_2016, GE_2016_SP, by = "date", all.x = TRUE)
write.csv(df_GE_2016, file = "GE_2016", row.names = FALSE)


#2017
df_GE_2017 <- merge(GE_Tweets_2017, GE_2017_SP, by = "date", all.x = TRUE)
write.csv(df_GE_2017, file = "GE_2017", row.names = FALSE)

df_GE_2016
```

```{r}
df_GE_2017
```

```{r}
#Fiserv
#2016
df_Fiserv_2016 <- merge(Fiserv_Tweets_2016, Fiserv_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Fiserv_2016, file = "Fiserv_2016", row.names = FALSE)

#2018
df_Fiserv_2018 <- merge(Fiserv_Tweets_2018, Fiserv_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Fiserv_2018, file = "Fiserv_2018", row.names = FALSE)

df_Fiserv_2016
```

```{r}
df_Fiserv_2018
```

```{r}
#Waste_Management
#2018
df_WM_2018 <- merge(WM_Tweets_2018, WM_SP_2018, by = "date", all.x = TRUE)
write.csv(df_WM_2018, file = "WM_2018", row.names = FALSE)
df_WM_2018
```

```{r}
#Wills_Tower
#2016
df_Wills_2016 <- merge(Wills_Tower_Tweets_2016, Wills_Tower_SP_2016, by = "date", all.x = TRUE)
write.csv(df_Wills_2016, file = "Wills_2016", row.names = FALSE)

#2018
df_Wills_2018 <- merge(Wills_Tower_Tweets_2018, WLTW_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Wills_2018, file = "Wills_2018", row.names = FALSE)

df_Wills_2016
```

```{r}
df_Wills_2018
```

```{r}
#Tripadvisor
#2016
df_tripadvisor_2016 <- merge(Tripadvisor_Tweets_2016, Tripadvisor_SP_2016, by = "date", all.x = TRUE)
write.csv(df_tripadvisor_2016, file = "Tripadvisor_2016", row.names = FALSE)

#2018
df_tripadvisor_2018 <- merge(Tripadvisor_Tweets_2018, Tripadvisor_SP_2018, by = "date", all.x = TRUE)
write.csv(df_tripadvisor_2018, file = "Tripadvisor_2018", row.names = FALSE)

df_tripadvisor_2016
```

```{r}
df_tripadvisor_2018
```

```{r}
#Davita_Kent
#2016
df_DavitaK_2016 <- merge(Davita_Tweets_2016K, Davita_SP_2016, by = "date", all.x = TRUE)
write.csv(df_DavitaK_2016, file = "Davita_K_2016", row.names = FALSE)

#2018
df_DavitaK_2018 <- merge(Davita_Tweets_2018K, Davita_SP_2018, by = "date", all.x = TRUE)
write.csv(df_DavitaK_2018, file = "Davita_K_2018", row.names = FALSE)

df_DavitaK_2016
```

```{r}
df_DavitaK_2018
```

```{r}
#Starbucks
df_Starbucks_2018 <- merge(Starbucks_Tweets_2018, Starbucks_SP_2018, by = "date", all.x = TRUE)
write.csv(df_Starbucks_2018, file = "Starbucks_2018", row.names = FALSE)

df_Starbucks_2018
```

```{r}
#McCormick 
#2018
df_McCormick_2018 <- merge(McCormick_Tweets_2018, McCormick_SP_2018, by = "date", all.x = TRUE)
write.csv(df_McCormick_2018, file = "McCormick_2018", row.names = FALSE)

df_McCormick_2018
```

```{r}
#IHS Markit 
#2018
df_IHS_2018 <- merge(IHS_Tweets_2018, IHS_SP_2018, by = "date", all.x = TRUE)
write.csv(df_IHS_2018, file = "IHS_2018", row.names = FALSE)

df_IHS_2018
```

```{r}
#AMD
#2018
df_AMD_2018 <- merge(AMD_Tweets_2018, AMD_SP_2018, by = "date", all.x = TRUE)
write.csv(df_AMD_2018, file = "AMD_2018", row.names = FALSE)

df_AMD_2018
```

```{r}
#ResMed
#2016
df_ResMed_2016 <-merge(ResMed_Tweets_2016, ResMed_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_ResMed_2016, file = "ResMed_2016", row.names = FALSE)

#2018
df_ResMed_2018 <-merge(ResMed_Tweets_2018, ResMed_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_ResMed_2018, file = "ResMed_2018", row.names = FALSE)

df_ResMed_2016
```

```{r}
df_ResMed_2018
```

```{r}
#CA
#2016
df_CA_2016 <-merge(CA_Tweets_2016, CA_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_CA_2016, file = "CA_2016", row.names = FALSE)

#2018
df_CA_2018 <-merge(CA_Tweets_2018, CA_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_CA_2018, file = "CA_2018", row.names = FALSE)

df_CA_2016
```

```{r}
df_CA_2018
```

```{r}
#General_Motors
#2016
df_GM_2016 <-merge(GM_Tweets_2016, GM_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_GM_2016, file = "GM_2016", row.names = FALSE)

#2018
df_GM_2018 <-merge(GM_Tweets_2018, GM_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_GM_2018, file = "GM_2018", row.names = FALSE)

df_GM_2016
```

```{r}
df_GM_2018
```

```{r}
#Aetna
#2016
df_Aetna_2016 <-merge(Aetna_Tweets_2016, AET_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Aetna_2016, file = "Aetna_2016", row.names = FALSE)

#2018
df_Aetna_2018 <-merge(Aetna_Tweets_2018, Aetna_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Aetna_2018, file = "Aetna_2018", row.names = FALSE)

df_Aetna_2016
```

```{r}
df_Aetna_2018
```

```{r}
#NRG
#2016
df_NRG_2016 <-merge(NRG_Tweets_2016, NRG_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_NRG_2016, file = "NRG_2016", row.names = FALSE)

#2018
df_NRG_2018 <-merge(NRG_Tweets_2018, NRG_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_NRG_2018, file = "NRG_2018", row.names = FALSE)

df_NRG_2016
```

```{r}
df_NRG_2018
```

```{r}
#Medtronic
#2016
df_Medtronic_2016 <-merge(Medtronic_Tweets_2016, Medtronic_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Medtronic_2016, file = "Medtronic_2016", row.names = FALSE)

#2018
df_Medtronic_2018 <-merge(Medtronic_Tweets_2018, Medtronic_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Medtronic_2018, file = "Medtronic_2018", row.names = FALSE)

df_Medtronic_2016
```

```{r}
df_Medtronic_2018
```

```{r}
#Juniper
#2016
df_Juniper_2016 <-merge(Juniper_Tweets_2016, Juniper_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Juniper_2016, file = "Juniper_2016", row.names = FALSE)

#2018
df_Juniper_2018 <-merge(Juniper_Tweets_2018, Juniper_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Juniper_2018, file = "Juniper_2018", row.names = FALSE)

df_Juniper_2016
```

```{r}
df_Juniper_2018
```

```{r}
#Aetna_Ramon_2018
#2018
df_AetnaR_2018 <-merge(Aetna_Tweets_2018R, AetnaR_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_AetnaR_2018, file = "AetnaR_2018", row.names = FALSE)

df_AetnaR_2018
```

```{r}
#Netflix
#2016
df_Netflix_2016 <-merge(Netflix_Tweets_2016, Netflix_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Netflix_2016, file = "Netflix_2016", row.names = FALSE)

df_Netflix_2016
```

```{r}
#Disney
df_Disney_2018 <-merge(Disney_Tweets_2018, Disney_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Disney_2018, file = "Disney_2018", row.names = FALSE)

df_Disney_2018
```

```{r}
#FOX
#2015
df_Fox_2015 <-merge(Fox_Tweets_2015, Fox_SP_2015, by = "date", all.x = TRUE) 
write.csv(df_Fox_2015, file = "Fox_2015", row.names = FALSE)

df_Fox_2015
```

```{r}
#Microsoft
#2016
df_Microsoft_2016 <-merge(Microsoft_Tweets_2016, Microsoft_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Microsoft_2016, file = "Microsoft_2016", row.names = FALSE)

#2018
df_Microsoft_2018 <-merge(Microsoft_Tweets_2018, Microsoft_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Microsoft_2018, file = "Microsoft_2018", row.names = FALSE)

df_Microsoft_2016
```

```{r}
df_Microsoft_2018
```

```{r}
#Juniper_Shaygan
#2014
df_Juniper_S_2014 <-merge(JuniperS_Tweets_2014, Juniper_SP_2014, by = "date", all.x = TRUE) 
write.csv(df_Juniper_S_2014, file = "Juniper_S_2014", row.names = FALSE)


df_Juniper_S_2014

```

```{r}
df_Juniper_S_2016
```

```{r}
#Synchrony Financial 
#2016
df_SYFM_2016 <-merge(SYFM_Tweets_2016, SYF_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_SYFM_2016, file = "SYFM_2016", row.names = FALSE)

#2018
df_SYFM_2018 <-merge(SYFM_Tweets_2018, SYFM_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_SYFM_2018, file = "SYFM_2018", row.names = FALSE)

df_SYFM_2016
```

```{r}
df_SYFM_2018
```

```{r}
#Southern Company
#2016
df_Southern_2016 <-merge(SouthernC_Tweets_2016, SO_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Southern_2016, file = "Southern_2016", row.names = FALSE)

#2018
df_Southern_2018 <-merge(SouthernC_Tweets_2018, SO_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Southern_2018, file = "Southern_2018", row.names = FALSE)

df_Southern_2016
```

```{r}
df_Southern_2018
```

```{r}
#Apple
#2016
df_Apple_2016 <-merge(Apple_Tweets_2016, Apple_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Apple_2016, file = "Apple_2016", row.names = FALSE)

#2018
df_Apple_2018 <-merge(Apple_Tweets_2018, Apple_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Apple_2018, file = "Apple_2018", row.names = FALSE)

df_Apple_2016
```

```{r}
df_Apple_2018
```

```{r}
#XL
#2016
df_XL_2016 <-merge(XL_Tweets_2016, XL_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_XL_2016, file = "XL_2016", row.names = FALSE)

#2018
df_XL_2018 <-merge(XL_Tweets_2018, XL_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_XL_2018, file = "XL_2018", row.names = FALSE)

df_XL_2016
```

```{r}
df_XL_2018
```

```{r}
#Tysonfoods
#2016
df_Tysonf_2016 <-merge(TSN_Tweets_2016, TSN_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Tysonf_2016, file = "TSN_2016", row.names = FALSE)

#2018
df_Tysonf_2018 <-merge(TSN_Tweets_2018, TSN_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Tysonf_2018, file = "TSN_2018", row.names = FALSE)

df_Tysonf_2016
```

```{r}
df_Tysonf_2018
```

```{r}
#Akamai
#2016
df_Akamai_2016 <-merge(Akamai_Tweets_2016, Akamai_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_Akamai_2016, file = "Akamai_2016", row.names = FALSE)

#2018
df_Akamai_2018 <-merge(Akamai_Tweets_2018, Akamai_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_Akamai_2018, file = "Akamai_2018", row.names = FALSE)

df_Akamai_2016
```

```{r}
df_Akamai_2018
```

###Clean

```{r}
#Packages
library(tm)
library(stringr)
install.packages("textstem")
library("textstem")




```

```{r}
#Function to clean the tweets
clean_tweets <- function(tweets) {
  
  # Convert text to lower case
  tweets <- tolower(tweets)
  
  # Remove URLs
  tweets <- stringr::str_replace_all(tweets, "http[^[:space:]]*", "")
  
  # Remove mentions
  tweets <- stringr::str_replace_all(tweets, "@[^[:space:]]*", "")
  
  # Remove hashtags
  tweets <- stringr::str_replace_all(tweets, "#[^[:space:]]*", "")
  
  # Remove punctuation marks
  tweets <- stringr::str_replace_all(tweets, "[[:punct:]]", "")
  
  # Remove numbers
  tweets <- stringr::str_replace_all(tweets, "[[:digit:]]", "")
  
  # Remove common stop words
  tweets <- tm::removeWords(tweets, tm::stopwords("en"))
  
  
  return(tweets)
}

df_Test$text <- clean_tweets(df_Test$text)

```

```{r}
#Lemmatize
df_Test$text <- lemmatize_words(df_Test$text)


```

#Numeric NA values & Tweets 

```{r}
#Johnson
#Remove columns 
df_Johnson_2016 <- df_Johnson_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Johnson_2016

#Ticker 
df_Johnson_2016$TICKER[is.na(df_Johnson_2016$TICKER)] <- "JCI"

#PRC
PRC_AVG <- mean(df_Johnson_2016$PRC, na.rm = TRUE)
df_Johnson_2016$PRC[is.na(df_Johnson_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Johnson_2016$VOL, na.rm = TRUE)
df_Johnson_2016$VOL[is.na(df_Johnson_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Johnson_2016$BID, na.rm = TRUE)
df_Johnson_2016$BID[is.na(df_Johnson_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Johnson_2016$ASK, na.rm = TRUE)
df_Johnson_2016$ASK[is.na(df_Johnson_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Johnson_2016$OPENPRC, na.rm = TRUE)
df_Johnson_2016$OPENPRC[is.na(df_Johnson_2016$OPENPRC)] <- OPEN_AVG

df_Johnson_2016
```


```{r}
df_Johnson_2016$text <- clean_tweets(df_Johnson_2016$text)
df_Johnson_2016$text <- lemmatize_words(df_Johnson_2016$text)
```


```{r}
```

```{r}
#Remove columns 
df_Johnson_2017 <- df_Johnson_2017[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Johnson_2017

#Ticker 
df_Johnson_2017$TICKER[is.na(df_Johnson_2017$TICKER)] <- "JCI"

#PRC
PRC_AVG <- mean(df_Johnson_2017$PRC, na.rm = TRUE)
df_Johnson_2017$PRC[is.na(df_Johnson_2017$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Johnson_2017$VOL, na.rm = TRUE)
df_Johnson_2017$VOL[is.na(df_Johnson_2017$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Johnson_2017$BID, na.rm = TRUE)
df_Johnson_2017$BID[is.na(df_Johnson_2017$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Johnson_2017$ASK, na.rm = TRUE)
df_Johnson_2017$ASK[is.na(df_Johnson_2017$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Johnson_2017$OPENPRC, na.rm = TRUE)
df_Johnson_2017$OPENPRC[is.na(df_Johnson_2017$OPENPRC)] <- OPEN_AVG

df_Johnson_2017
```
```{r}
df_Johnson_2017$text <- clean_tweets(df_Johnson_2017$text)
df_Johnson_2017$text <- lemmatize_words(df_Johnson_2017$text)
```

```{r}
#NASDAQ
#Remove columns 
df_NASDAQ <- df_NASDAQ[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_NASDAQ

#Ticker 
df_NASDAQ$TICKER[is.na(df_NASDAQ$TICKER)] <- "NDAQ"

#PRC
PRC_AVG <- mean(df_NASDAQ$PRC, na.rm = TRUE)
df_NASDAQ$PRC[is.na(df_NASDAQ$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_NASDAQ$VOL, na.rm = TRUE)
df_NASDAQ$VOL[is.na(df_NASDAQ$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_NASDAQ$BID, na.rm = TRUE)
df_NASDAQ$BID[is.na(df_NASDAQ$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_NASDAQ$ASK, na.rm = TRUE)
df_NASDAQ$ASK[is.na(df_NASDAQ$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_NASDAQ$OPENPRC, na.rm = TRUE)
df_NASDAQ$OPENPRC[is.na(df_NASDAQ$OPENPRC)] <- OPEN_AVG

df_NASDAQ
```
```{r}
df_NASDAQ$text <- clean_tweets(df_NASDAQ$text)
df_NASDAQ$text <- lemmatize_words(df_NASDAQ$text)
```


```{r}
```
```{r}
#Autodesk
#Remove columns 
df_autodesk <- df_autodesk[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_autodesk

#Ticker 
df_autodesk$TICKER[is.na(df_autodesk$TICKER)] <- "ADSK"

#PRC
PRC_AVG <- mean(df_autodesk$PRC, na.rm = TRUE)
df_autodesk$PRC[is.na(df_autodesk$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_autodesk$VOL, na.rm = TRUE)
df_autodesk$VOL[is.na(df_autodesk$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_autodesk$BID, na.rm = TRUE)
df_autodesk$BID[is.na(df_autodesk$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_autodesk$ASK, na.rm = TRUE)
df_autodesk$ASK[is.na(df_autodesk$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_autodesk$OPENPRC, na.rm = TRUE)
df_autodesk$OPENPRC[is.na(df_autodesk$OPENPRC)] <- OPEN_AVG

df_autodesk
```           
```{r}
df_autodesk$text <- clean_tweets(df_autodesk$text)
df_autodesk$text <- lemmatize_words(df_autodesk$text)
```

```{r}
#Hasbro 
#Remove columns 
df_Hasbro_2016 <- df_Hasbro_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Hasbro_2016 

#Ticker 
df_Hasbro_2016$TICKER[is.na(df_Hasbro_2016$TICKER)] <- "HAS"

#PRC
PRC_AVG <- mean(df_Hasbro_2016$PRC, na.rm = TRUE)
df_Hasbro_2016$PRC[is.na(df_Hasbro_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Hasbro_2016$VOL, na.rm = TRUE)
df_Hasbro_2016$VOL[is.na(df_Hasbro_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Hasbro_2016$BID, na.rm = TRUE)
df_Hasbro_2016$BID[is.na(df_Hasbro_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Hasbro_2016$ASK, na.rm = TRUE)
df_Hasbro_2016$ASK[is.na(df_Hasbro_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Hasbro_2016$OPENPRC, na.rm = TRUE)
df_Hasbro_2016$OPENPRC[is.na(df_Hasbro_2016$OPENPRC)] <- OPEN_AVG

df_Hasbro_2016
```
```{r}
df_Hasbro_2016$text <- clean_tweets(df_Hasbro_2016$text)
df_Hasbro_2016$text <- lemmatize_words(df_Hasbro_2016$text)
```

```{r}
#Remove columns 
df_Hasbro_2018 <- df_Hasbro_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Hasbro_2018 

#Ticker 
df_Hasbro_2018$TICKER[is.na(df_Hasbro_2018$TICKER)] <- "HAS"

#PRC
PRC_AVG <- mean(df_Hasbro_2018$PRC, na.rm = TRUE)
df_Hasbro_2018$PRC[is.na(df_Hasbro_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Hasbro_2018$VOL, na.rm = TRUE)
df_Hasbro_2018$VOL[is.na(df_Hasbro_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Hasbro_2018$BID, na.rm = TRUE)
df_Hasbro_2018$BID[is.na(df_Hasbro_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Hasbro_2018$ASK, na.rm = TRUE)
df_Hasbro_2018$ASK[is.na(df_Hasbro_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Hasbro_2018$OPENPRC, na.rm = TRUE)
df_Hasbro_2018$OPENPRC[is.na(df_Hasbro_2018$OPENPRC)] <- OPEN_AVG

df_Hasbro_2018
```


```{r}
df_Hasbro_2018$text <- clean_tweets(df_Hasbro_2018$text)
df_Hasbro_2018$text <- lemmatize_words(df_Hasbro_2018$text)
```
```{r}
#Intel 
#Remove columns 
df_Intel_2016 <- df_Intel_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Intel_2016 

#Ticker 
df_Intel_2016$TICKER[is.na(df_Intel_2016$TICKER)] <- "INTC"

#PRC
PRC_AVG <- mean(df_Intel_2016$PRC, na.rm = TRUE)
df_Intel_2016$PRC[is.na(df_Intel_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Intel_2016$VOL, na.rm = TRUE)
df_Intel_2016$VOL[is.na(df_Intel_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Intel_2016$BID, na.rm = TRUE)
df_Intel_2016$BID[is.na(df_Intel_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Intel_2016$ASK, na.rm = TRUE)
df_Intel_2016$ASK[is.na(df_Intel_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Intel_2016$OPENPRC, na.rm = TRUE)
df_Intel_2016$OPENPRC[is.na(df_Intel_2016$OPENPRC)] <- OPEN_AVG

df_Intel_2016
```
```{r}
df_Intel_2016$text <- clean_tweets(df_Intel_2016$text)
df_Intel_2016$text <- lemmatize_words(df_Intel_2016$text)
```

```{r}
#Remove columns 
df_Intel_2018 <- df_Intel_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Intel_2018 

#Ticker 
df_Intel_2018$TICKER[is.na(df_Intel_2018$TICKER)] <- "INTC"

#PRC
PRC_AVG <- mean(df_Intel_2018$PRC, na.rm = TRUE)
df_Intel_2018$PRC[is.na(df_Intel_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Intel_2018$VOL, na.rm = TRUE)
df_Intel_2018$VOL[is.na(df_Intel_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Intel_2018$BID, na.rm = TRUE)
df_Intel_2018$BID[is.na(df_Intel_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Intel_2018$ASK, na.rm = TRUE)
df_Intel_2018$ASK[is.na(df_Intel_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Intel_2018$OPENPRC, na.rm = TRUE)
df_Intel_2018$OPENPRC[is.na(df_Intel_2018$OPENPRC)] <- OPEN_AVG

df_Intel_2018
```
```{r}
df_Intel_2018$text <- clean_tweets(df_Intel_2018$text)
df_Intel_2018$text <- lemmatize_words(df_Intel_2018$text)
```

```{r}
#Activision
#Remove columns 
df_Activision_2016 <- df_Activision_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Activision_2016 

#Ticker 
df_Activision_2016$TICKER[is.na(df_Activision_2016$TICKER)] <- "ATVI"

#PRC
PRC_AVG <- mean(df_Activision_2016$PRC, na.rm = TRUE)
df_Activision_2016$PRC[is.na(df_Activision_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Activision_2016$VOL, na.rm = TRUE)
df_Activision_2016$VOL[is.na(df_Activision_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Activision_2016$BID, na.rm = TRUE)
df_Activision_2016$BID[is.na(df_Activision_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Activision_2016$ASK, na.rm = TRUE)
df_Activision_2016$ASK[is.na(df_Activision_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Activision_2016$OPENPRC, na.rm = TRUE)
df_Activision_2016$OPENPRC[is.na(df_Activision_2016$OPENPRC)] <- OPEN_AVG

df_Activision_2016
```
```{r}
df_Activision_2016$text <- clean_tweets(df_Activision_2016$text)
df_Activision_2016$text <- lemmatize_words(df_Activision_2016$text)
```


```{r}
#Remove columns 
df_Activision_2018 <- df_Activision_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Activision_2018 

#Ticker 
df_Activision_2018$TICKER[is.na(df_Activision_2018$TICKER)] <- "ATVI"

#PRC
PRC_AVG <- mean(df_Activision_2018$PRC, na.rm = TRUE)
df_Activision_2018$PRC[is.na(df_Activision_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Activision_2018$VOL, na.rm = TRUE)
df_Activision_2018$VOL[is.na(df_Activision_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Activision_2018$BID, na.rm = TRUE)
df_Activision_2018$BID[is.na(df_Activision_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Activision_2018$ASK, na.rm = TRUE)
df_Activision_2018$ASK[is.na(df_Activision_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Activision_2018$OPENPRC, na.rm = TRUE)
df_Activision_2018$OPENPRC[is.na(df_Activision_2018$OPENPRC)] <- OPEN_AVG

df_Activision_2018
```
```{r}
df_Activision_2018$text <- clean_tweets(df_Activision_2018$text)
df_Activision_2018$text <- lemmatize_words(df_Activision_2018$text)
```

```{r}
#Inuit
#Remove columns 
df_Inuit_2016 <- df_Inuit_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Inuit_2016 

#Ticker 
df_Inuit_2016$TICKER[is.na(df_Inuit_2016$TICKER)] <- "INTU"

#PRC
PRC_AVG <- mean(df_Inuit_2016$PRC, na.rm = TRUE)
df_Inuit_2016$PRC[is.na(df_Inuit_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Inuit_2016$VOL, na.rm = TRUE)
df_Inuit_2016$VOL[is.na(df_Inuit_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Inuit_2016$BID, na.rm = TRUE)
df_Inuit_2016$BID[is.na(df_Inuit_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Inuit_2016$ASK, na.rm = TRUE)
df_Inuit_2016$ASK[is.na(df_Inuit_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Inuit_2016$OPENPRC, na.rm = TRUE)
df_Inuit_2016$OPENPRC[is.na(df_Inuit_2016$OPENPRC)] <- OPEN_AVG

df_Inuit_2016
```
```{r}
df_Inuit_2016$text <- clean_tweets(df_Inuit_2016$text)
df_Inuit_2016$text <- lemmatize_words(df_Inuit_2016$text)
```

```{r}
#Remove columns 
df_Inuit_2018 <- df_Inuit_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Inuit_2018


#Ticker 
df_Inuit_2018$TICKER[is.na(df_Inuit_2018$TICKER)] <- "INTU"

#PRC
PRC_AVG <- mean(df_Inuit_2018$PRC, na.rm = TRUE)
df_Inuit_2018$PRC[is.na(df_Inuit_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Inuit_2018$VOL, na.rm = TRUE)
df_Inuit_2018$VOL[is.na(df_Inuit_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Inuit_2018$BID, na.rm = TRUE)
df_Inuit_2018$BID[is.na(df_Inuit_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Inuit_2018$ASK, na.rm = TRUE)
df_Inuit_2018$ASK[is.na(df_Inuit_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Inuit_2018$OPENPRC, na.rm = TRUE)
df_Inuit_2018$OPENPRC[is.na(df_Inuit_2018$OPENPRC)] <- OPEN_AVG

df_Inuit_2018
```
```{r}
df_Inuit_2018$text <- clean_tweets(df_Inuit_2018$text)
df_Inuit_2018$text <- lemmatize_words(df_Inuit_2018$text)
```

```{r}
#Allergan 
#Remove columns 
df_Allergan_2016 <- df_Allergan_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Allergan_2016

#Ticker 
df_Allergan_2016$TICKER[is.na(df_Allergan_2016$TICKER)] <- "AGN"

#PRC
PRC_AVG <- mean(df_Allergan_2016$PRC, na.rm = TRUE)
df_Allergan_2016$PRC[is.na(df_Allergan_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Allergan_2016$VOL, na.rm = TRUE)
df_Allergan_2016$VOL[is.na(df_Allergan_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Allergan_2016$BID, na.rm = TRUE)
df_Allergan_2016$BID[is.na(df_Allergan_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Allergan_2016$ASK, na.rm = TRUE)
df_Allergan_2016$ASK[is.na(df_Allergan_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Allergan_2016$OPENPRC, na.rm = TRUE)
df_Allergan_2016$OPENPRC[is.na(df_Allergan_2016$OPENPRC)] <- OPEN_AVG

df_Allergan_2016
```
```{r}
df_Allergan_2016$text <- clean_tweets(df_Allergan_2016$text)
df_Allergan_2016$text <- lemmatize_words(df_Allergan_2016$text)
```

```{r}
#Remove columns 
df_Allergan_2018 <- df_Allergan_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Allergan_2018

#Ticker 
df_Allergan_2018$TICKER[is.na(df_Allergan_2018$TICKER)] <- "AGN"

#PRC
PRC_AVG <- mean(df_Allergan_2018$PRC, na.rm = TRUE)
df_Allergan_2018$PRC[is.na(df_Allergan_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Allergan_2018$VOL, na.rm = TRUE)
df_Allergan_2018$VOL[is.na(df_Allergan_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Allergan_2018$BID, na.rm = TRUE)
df_Allergan_2018$BID[is.na(df_Allergan_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Allergan_2018$ASK, na.rm = TRUE)
df_Allergan_2018$ASK[is.na(df_Allergan_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Allergan_2018$OPENPRC, na.rm = TRUE)
df_Allergan_2018$OPENPRC[is.na(df_Allergan_2018$OPENPRC)] <- OPEN_AVG

df_Allergan_2018
```


```{r}
df_Allergan_2018$text <- clean_tweets(df_Allergan_2018$text)
df_Allergan_2018$text <- lemmatize_words(df_Allergan_2018$text)
```

```{r}
#Humana
#Remove columns 
df_Humana_2016 <- df_Humana_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Humana_2016

#Ticker 
df_Humana_2016$TICKER[is.na(df_Humana_2016$TICKER)] <- "HUM"

#PRC
PRC_AVG <- mean(df_Humana_2016$PRC, na.rm = TRUE)
df_Humana_2016$PRC[is.na(df_Humana_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Humana_2016$VOL, na.rm = TRUE)
df_Humana_2016$VOL[is.na(df_Humana_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Humana_2016$BID, na.rm = TRUE)
df_Humana_2016$BID[is.na(df_Humana_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Humana_2016$ASK, na.rm = TRUE)
df_Humana_2016$ASK[is.na(df_Humana_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Humana_2016$OPENPRC, na.rm = TRUE)
df_Humana_2016$OPENPRC[is.na(df_Humana_2016$OPENPRC)] <- OPEN_AVG

df_Humana_2016
```


```{r}
df_Humana_2016$text <- clean_tweets(df_Humana_2016$text)
df_Humana_2016$text <- lemmatize_words(df_Humana_2016$text)
```
```{r}
#Remove columns 
df_Humana_2018 <- df_Humana_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Humana_2018

#Ticker 
df_Humana_2018$TICKER[is.na(df_Humana_2018$TICKER)] <- "HUM"

#PRC
PRC_AVG <- mean(df_Humana_2018$PRC, na.rm = TRUE)
df_Humana_2018$PRC[is.na(df_Humana_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Humana_2018$VOL, na.rm = TRUE)
df_Humana_2018$VOL[is.na(df_Humana_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Humana_2018$BID, na.rm = TRUE)
df_Humana_2018$BID[is.na(df_Humana_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Humana_2018$ASK, na.rm = TRUE)
df_Humana_2018$ASK[is.na(df_Humana_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Humana_2018$OPENPRC, na.rm = TRUE)
df_Humana_2018$OPENPRC[is.na(df_Humana_2018$OPENPRC)] <- OPEN_AVG

df_Humana_2018

```
```{r}
df_Humana_2018$text <- clean_tweets(df_Humana_2018$text)
df_Humana_2018$text <- lemmatize_words(df_Humana_2018$text)
```

```{r}
#CB Autodesk 
#Remove columns 
df_CB_autodesk_2016 <- df_CB_autodesk_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_CB_autodesk_2016

#Ticker 
df_CB_autodesk_2016$TICKER[is.na(df_CB_autodesk_2016$TICKER)] <- "ADSK"

#PRC
PRC_AVG <- mean(df_CB_autodesk_2016$PRC, na.rm = TRUE)
df_CB_autodesk_2016$PRC[is.na(df_CB_autodesk_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_CB_autodesk_2016$VOL, na.rm = TRUE)
df_CB_autodesk_2016$VOL[is.na(df_CB_autodesk_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_CB_autodesk_2016$BID, na.rm = TRUE)
df_CB_autodesk_2016$BID[is.na(df_CB_autodesk_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_CB_autodesk_2016$ASK, na.rm = TRUE)
df_CB_autodesk_2016$ASK[is.na(df_CB_autodesk_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CB_autodesk_2016$OPENPRC, na.rm = TRUE)
df_CB_autodesk_2016$OPENPRC[is.na(df_CB_autodesk_2016$OPENPRC)] <- OPEN_AVG

df_CB_autodesk_2016
```
```{r}
df_CB_autodesk_2016$text <- clean_tweets(df_CB_autodesk_2016$text)
df_CB_autodesk_2016$text <- lemmatize_words(df_CB_autodesk_2016$text)
```

```{r}
#Remove columns 
df_CB_autodesk_2018 <- df_CB_autodesk_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_CB_autodesk_2018

#Ticker 
df_CB_autodesk_2018$TICKER[is.na(df_CB_autodesk_2018$TICKER)] <- "ADSK"

#PRC
PRC_AVG <- mean(df_CB_autodesk_2018$PRC, na.rm = TRUE)
df_CB_autodesk_2018$PRC[is.na(df_CB_autodesk_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_CB_autodesk_2018$VOL, na.rm = TRUE)
df_CB_autodesk_2018$VOL[is.na(df_CB_autodesk_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_CB_autodesk_2018$BID, na.rm = TRUE)
df_CB_autodesk_2018$BID[is.na(df_CB_autodesk_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_CB_autodesk_2018$ASK, na.rm = TRUE)
df_CB_autodesk_2018$ASK[is.na(df_CB_autodesk_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CB_autodesk_2018$OPENPRC, na.rm = TRUE)
df_CB_autodesk_2018$OPENPRC[is.na(df_CB_autodesk_2018$OPENPRC)] <- OPEN_AVG

df_CB_autodesk_2018
```


```{r}
df_CB_autodesk_2018$text <- clean_tweets(df_CB_autodesk_2018$text)
df_CB_autodesk_2018$text <- lemmatize_words(df_CB_autodesk_2018$text)
```
```{r}
#Equinox
#Remove columns 
df_equinox_2018 <- df_equinox_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_equinox_2018

#Ticker 
df_equinox_2018$TICKER[is.na(df_equinox_2018$TICKER)] <- "EQIX"

#PRC
PRC_AVG <- mean(df_equinox_2018$PRC, na.rm = TRUE)
df_equinox_2018$PRC[is.na(df_equinox_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_equinox_2018$VOL, na.rm = TRUE)
df_equinox_2018$VOL[is.na(df_equinox_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_equinox_2018$BID, na.rm = TRUE)
df_equinox_2018$BID[is.na(df_equinox_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_equinox_2018$ASK, na.rm = TRUE)
df_equinox_2018$ASK[is.na(df_equinox_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_equinox_2018$OPENPRC, na.rm = TRUE)
df_equinox_2018$OPENPRC[is.na(df_equinox_2018$OPENPRC)] <- OPEN_AVG

df_equinox_2018
```
```{r}
df_equinox_2018$text <- clean_tweets(df_equinox_2018$text)
df_equinox_2018$text <- lemmatize_words(df_equinox_2018$text)
```

```{r}
#Cisco
#Remove columns 
df_cisco_2016 <- df_cisco_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_cisco_2016

#Ticker 
df_cisco_2016$TICKER[is.na(df_cisco_2016$TICKER)] <- "CSCO"

#PRC
PRC_AVG <- mean(df_cisco_2016$PRC, na.rm = TRUE)
df_cisco_2016$PRC[is.na(df_cisco_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_cisco_2016$VOL, na.rm = TRUE)
df_cisco_2016$VOL[is.na(df_cisco_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_cisco_2016$BID, na.rm = TRUE)
df_cisco_2016$BID[is.na(df_cisco_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_cisco_2016$ASK, na.rm = TRUE)
df_cisco_2016$ASK[is.na(df_cisco_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_cisco_2016$OPENPRC, na.rm = TRUE)
df_cisco_2016$OPENPRC[is.na(df_cisco_2016$OPENPRC)] <- OPEN_AVG

df_cisco_2016
```
```{r}
df_cisco_2016$text <- clean_tweets(df_cisco_2016$text)
df_cisco_2016$text <- lemmatize_words(df_cisco_2016$text)
```

```{r}
#Remove columns 
df_cisco_2018 <- df_cisco_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_cisco_2018

#Ticker 
df_cisco_2018$TICKER[is.na(df_cisco_2018$TICKER)] <- "CSCO"

#PRC
PRC_AVG <- mean(df_cisco_2018$PRC, na.rm = TRUE)
df_cisco_2018$PRC[is.na(df_cisco_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_cisco_2018$VOL, na.rm = TRUE)
df_cisco_2018$VOL[is.na(df_cisco_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_cisco_2018$BID, na.rm = TRUE)
df_cisco_2018$BID[is.na(df_cisco_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_cisco_2018$ASK, na.rm = TRUE)
df_cisco_2018$ASK[is.na(df_cisco_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_cisco_2018$OPENPRC, na.rm = TRUE)
df_cisco_2018$OPENPRC[is.na(df_cisco_2018$OPENPRC)] <- OPEN_AVG

df_cisco_2018
```


```{r}
df_cisco_2018$text <- clean_tweets(df_cisco_2018$text)
df_cisco_2018$text <- lemmatize_words(df_cisco_2018$text)
```

```{r}
#EBAY
#Remove columns 
df_EBAY_2016 <- df_EBAY_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_EBAY_2016

#Ticker 
df_EBAY_2016$TICKER[is.na(df_EBAY_2016$TICKER)] <- "EBAY"

#PRC
PRC_AVG <- mean(df_EBAY_2016$PRC, na.rm = TRUE)
df_EBAY_2016$PRC[is.na(df_EBAY_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_EBAY_2016$VOL, na.rm = TRUE)
df_EBAY_2016$VOL[is.na(df_EBAY_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_EBAY_2016$BID, na.rm = TRUE)
df_EBAY_2016$BID[is.na(df_EBAY_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_EBAY_2016$ASK, na.rm = TRUE)
df_EBAY_2016$ASK[is.na(df_EBAY_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_EBAY_2016$OPENPRC, na.rm = TRUE)
df_EBAY_2016$OPENPRC[is.na(df_EBAY_2016$OPENPRC)] <- OPEN_AVG

df_EBAY_2016
```
```{r}
df_EBAY_2016$text <- clean_tweets(df_EBAY_2016$text)
df_EBAY_2016$text <- lemmatize_words(df_EBAY_2016$text)
```

```{r}
#Remove columns 
df_EBAY_2018 <- df_EBAY_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_EBAY_2018

#Ticker 
df_EBAY_2018$TICKER[is.na(df_EBAY_2018$TICKER)] <- "EBAY"

#PRC
PRC_AVG <- mean(df_EBAY_2018$PRC, na.rm = TRUE)
df_EBAY_2018$PRC[is.na(df_EBAY_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_EBAY_2018$VOL, na.rm = TRUE)
df_EBAY_2018$VOL[is.na(df_EBAY_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_EBAY_2018$BID, na.rm = TRUE)
df_EBAY_2018$BID[is.na(df_EBAY_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_EBAY_2018$ASK, na.rm = TRUE)
df_EBAY_2018$ASK[is.na(df_EBAY_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_EBAY_2018$OPENPRC, na.rm = TRUE)
df_EBAY_2018$OPENPRC[is.na(df_EBAY_2018$OPENPRC)] <- OPEN_AVG

df_EBAY_2018
```
```{r}
df_EBAY_2018$text <- clean_tweets(df_EBAY_2018$text)
df_EBAY_2018$text <- lemmatize_words(df_EBAY_2018$text)
```

```{r}
#Davita
#Remove columns 
df_Davita_2016 <- df_Davita_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Davita_2016

#Ticker 
df_Davita_2016$TICKER[is.na(df_Davita_2016$TICKER)] <- "DVA"

#PRC
PRC_AVG <- mean(df_Davita_2016$PRC, na.rm = TRUE)
df_Davita_2016$PRC[is.na(df_Davita_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Davita_2016$VOL, na.rm = TRUE)
df_Davita_2016$VOL[is.na(df_Davita_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Davita_2016$BID, na.rm = TRUE)
df_Davita_2016$BID[is.na(df_Davita_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Davita_2016$ASK, na.rm = TRUE)
df_Davita_2016$ASK[is.na(df_Davita_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Davita_2016$OPENPRC, na.rm = TRUE)
df_Davita_2016$OPENPRC[is.na(df_Davita_2016$OPENPRC)] <- OPEN_AVG

df_Davita_2016
```
```{r}
df_Davita_2016$text <- clean_tweets(df_Davita_2016$text)
df_Davita_2016$text <- lemmatize_words(df_Davita_2016$text)
```

```{r}
#Remove columns 
df_Davita_2017 <- df_Davita_2017[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Davita_2017

#Ticker 
df_Davita_2017$TICKER[is.na(df_Davita_2017$TICKER)] <- "DVA"

#PRC
PRC_AVG <- mean(df_Davita_2017$PRC, na.rm = TRUE)
df_Davita_2017$PRC[is.na(df_Davita_2017$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Davita_2017$VOL, na.rm = TRUE)
df_Davita_2017$VOL[is.na(df_Davita_2017$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Davita_2017$BID, na.rm = TRUE)
df_Davita_2017$BID[is.na(df_Davita_2017$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Davita_2017$ASK, na.rm = TRUE)
df_Davita_2017$ASK[is.na(df_Davita_2017$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Davita_2017$OPENPRC, na.rm = TRUE)
df_Davita_2017$OPENPRC[is.na(df_Davita_2017$OPENPRC)] <- OPEN_AVG

df_Davita_2017
```
```{r}
df_Davita_2017$text <- clean_tweets(df_Davita_2017$text)
df_Davita_2017$text <- lemmatize_words(df_Davita_2017$text)
```

```{r}
#Illumina 
#Remove columns 
df_Illumina_2016 <- df_Illumina_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Illumina_2016

#Ticker 
df_Illumina_2016$TICKER[is.na(df_Illumina_2016$TICKER)] <- "ILMN"

#PRC
PRC_AVG <- mean(df_Illumina_2016$PRC, na.rm = TRUE)
df_Illumina_2016$PRC[is.na(df_Illumina_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Illumina_2016$VOL, na.rm = TRUE)
df_Illumina_2016$VOL[is.na(df_Illumina_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Illumina_2016$BID, na.rm = TRUE)
df_Illumina_2016$BID[is.na(df_Illumina_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Illumina_2016$ASK, na.rm = TRUE)
df_Illumina_2016$ASK[is.na(df_Illumina_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Illumina_2016$OPENPRC, na.rm = TRUE)
df_Illumina_2016$OPENPRC[is.na(df_Illumina_2016$OPENPRC)] <- OPEN_AVG

df_Illumina_2016
```
```{r}
df_Illumina_2016$text <- clean_tweets(df_Illumina_2016$text)
df_Illumina_2016$text <- lemmatize_words(df_Illumina_2016$text)
```

```{r}
#Remove columns 
df_Illumina_2018 <- df_Illumina_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Illumina_2018

#Ticker 
df_Illumina_2018$TICKER[is.na(df_Illumina_2018$TICKER)] <- "ILMN"

#PRC
PRC_AVG <- mean(df_Illumina_2018$PRC, na.rm = TRUE)
df_Illumina_2018$PRC[is.na(df_Illumina_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Illumina_2018$VOL, na.rm = TRUE)
df_Illumina_2018$VOL[is.na(df_Illumina_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Illumina_2018$BID, na.rm = TRUE)
df_Illumina_2018$BID[is.na(df_Illumina_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Illumina_2018$ASK, na.rm = TRUE)
df_Illumina_2018$ASK[is.na(df_Illumina_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Illumina_2018$OPENPRC, na.rm = TRUE)
df_Illumina_2018$OPENPRC[is.na(df_Illumina_2018$OPENPRC)] <- OPEN_AVG

df_Illumina_2018
```


```{r}
df_Illumina_2018$text <- clean_tweets(df_Illumina_2018$text)
df_Illumina_2018$text <- lemmatize_words(df_Illumina_2018$text)
```


```{r}
```

```{r}
#Homedepot 
#Remove columns 
df_Homedepot_2013 <- df_Homedepot_2013[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Homedepot_2013

#Ticker 
df_Homedepot_2013$TICKER[is.na(df_Homedepot_2013$TICKER)] <- "HD"

#PRC
PRC_AVG <- mean(df_Homedepot_2013$PRC, na.rm = TRUE)
df_Homedepot_2013$PRC[is.na(df_Homedepot_2013$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Homedepot_2013$VOL, na.rm = TRUE)
df_Homedepot_2013$VOL[is.na(df_Homedepot_2013$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Homedepot_2013$BID, na.rm = TRUE)
df_Homedepot_2013$BID[is.na(df_Homedepot_2013$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Homedepot_2013$ASK, na.rm = TRUE)
df_Homedepot_2013$ASK[is.na(df_Homedepot_2013$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Homedepot_2013$OPENPRC, na.rm = TRUE)
df_Homedepot_2013$OPENPRC[is.na(df_Homedepot_2013$OPENPRC)] <- OPEN_AVG

df_Homedepot_2013
```
```{r}
df_Homedepot_2013$text <- clean_tweets(df_Homedepot_2013$text)
df_Homedepot_2013$text <- lemmatize_words(df_Homedepot_2013$text)
```

```{r}
#Southwest Airlines 
#Remove columns 
df_Southwest_2016 <- df_Southwest_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Southwest_2016

#Ticker 
df_Southwest_2016$TICKER[is.na(df_Southwest_2016$TICKER)] <- "LUV"

#PRC
PRC_AVG <- mean(df_Southwest_2016$PRC, na.rm = TRUE)
df_Southwest_2016$PRC[is.na(df_Southwest_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Southwest_2016$VOL, na.rm = TRUE)
df_Southwest_2016$VOL[is.na(df_Southwest_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Southwest_2016$BID, na.rm = TRUE)
df_Southwest_2016$BID[is.na(df_Southwest_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southwest_2016$ASK, na.rm = TRUE)
df_Southwest_2016$ASK[is.na(df_Southwest_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southwest_2016$OPENPRC, na.rm = TRUE)
df_Southwest_2016$OPENPRC[is.na(df_Southwest_2016$OPENPRC)] <- OPEN_AVG

df_Southwest_2016
```
```{r}
df_Southwest_2016$text <- clean_tweets(df_Southwest_2016$text)
df_Southwest_2016$text <- lemmatize_words(df_Southwest_2016$text)
```

```{r}
#Remove columns 
df_Southwest_2018 <- df_Southwest_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Southwest_2018

#Ticker 
df_Southwest_2018$TICKER[is.na(df_Southwest_2018$TICKER)] <- "LUV"

#PRC
PRC_AVG <- mean(df_Southwest_2018$PRC, na.rm = TRUE)
df_Southwest_2018$PRC[is.na(df_Southwest_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Southwest_2018$VOL, na.rm = TRUE)
df_Southwest_2018$VOL[is.na(df_Southwest_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Southwest_2018$BID, na.rm = TRUE)
df_Southwest_2018$BID[is.na(df_Southwest_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southwest_2018$ASK, na.rm = TRUE)
df_Southwest_2018$ASK[is.na(df_Southwest_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southwest_2018$OPENPRC, na.rm = TRUE)
df_Southwest_2018$OPENPRC[is.na(df_Southwest_2018$OPENPRC)] <- OPEN_AVG

df_Southwest_2018
```
```{r}
df_Southwest_2018$text <- clean_tweets(df_Southwest_2018$text)
df_Southwest_2018$text <- lemmatize_words(df_Southwest_2018$text)
```

```{r}
#FIS
#Remove columns 
df_FIS_2016 <- df_FIS_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_FIS_2016

#Ticker 
df_FIS_2016$TICKER[is.na(df_FIS_2016$TICKER)] <- "FIS"

#PRC
PRC_AVG <- mean(df_FIS_2016$PRC, na.rm = TRUE)
df_FIS_2016$PRC[is.na(df_FIS_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_FIS_2016$VOL, na.rm = TRUE)
df_FIS_2016$VOL[is.na(df_FIS_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_FIS_2016$BID, na.rm = TRUE)
df_FIS_2016$BID[is.na(df_FIS_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_FIS_2016$ASK, na.rm = TRUE)
df_FIS_2016$ASK[is.na(df_FIS_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_FIS_2016$OPENPRC, na.rm = TRUE)
df_FIS_2016$OPENPRC[is.na(df_FIS_2016$OPENPRC)] <- OPEN_AVG

df_FIS_2016
```
```{r}
df_FIS_2016$text <- clean_tweets(df_FIS_2016$text)
df_FIS_2016$text <- lemmatize_words(df_FIS_2016$text)
```

```{r}
#Remove columns 
df_FIS_2018 <- df_FIS_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_FIS_2018

#Ticker 
df_FIS_2018$TICKER[is.na(df_FIS_2018$TICKER)] <- "FIS"

#PRC
PRC_AVG <- mean(df_FIS_2018$PRC, na.rm = TRUE)
df_FIS_2018$PRC[is.na(df_FIS_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_FIS_2018$VOL, na.rm = TRUE)
df_FIS_2018$VOL[is.na(df_FIS_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_FIS_2018$BID, na.rm = TRUE)
df_FIS_2018$BID[is.na(df_FIS_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_FIS_2018$ASK, na.rm = TRUE)
df_FIS_2018$ASK[is.na(df_FIS_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_FIS_2018$OPENPRC, na.rm = TRUE)
df_FIS_2018$OPENPRC[is.na(df_FIS_2018$OPENPRC)] <- OPEN_AVG

df_FIS_2018
```


```{r}
df_FIS_2018$text <- clean_tweets(df_FIS_2018$text)
df_FIS_2018$text <- lemmatize_words(df_FIS_2018$text)
```


```{r}
```
```{r}
#Leucadia nation
#Remove columns 
df_Leucadia_2016 <- df_Leucadia_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Leucadia_2016

#Ticker 
df_Leucadia_2016$TICKER[is.na(df_Leucadia_2016$TICKER)] <- "LUK"

#PRC
PRC_AVG <- mean(df_Leucadia_2016$PRC, na.rm = TRUE)
df_Leucadia_2016$PRC[is.na(df_Leucadia_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Leucadia_2016$VOL, na.rm = TRUE)
df_Leucadia_2016$VOL[is.na(df_Leucadia_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Leucadia_2016$BID, na.rm = TRUE)
df_Leucadia_2016$BID[is.na(df_Leucadia_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Leucadia_2016$ASK, na.rm = TRUE)
df_Leucadia_2016$ASK[is.na(df_Leucadia_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Leucadia_2016$OPENPRC, na.rm = TRUE)
df_Leucadia_2016$OPENPRC[is.na(df_Leucadia_2016$OPENPRC)] <- OPEN_AVG

df_Leucadia_2016
```


```{r}
df_Leucadia_2016$text <- clean_tweets(df_Leucadia_2016$text)
df_Leucadia_2016$text <- lemmatize_words(df_Leucadia_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_Leucadia_2018 <- df_Leucadia_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Leucadia_2018

#Ticker 
df_Leucadia_2018$TICKER[is.na(df_Leucadia_2018$TICKER)] <- "LUK"

#PRC
PRC_AVG <- mean(df_Leucadia_2018$PRC, na.rm = TRUE)
df_Leucadia_2018$PRC[is.na(df_Leucadia_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Leucadia_2018$VOL, na.rm = TRUE)
df_Leucadia_2018$VOL[is.na(df_Leucadia_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Leucadia_2018$BID, na.rm = TRUE)
df_Leucadia_2018$BID[is.na(df_Leucadia_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Leucadia_2018$ASK, na.rm = TRUE)
df_Leucadia_2018$ASK[is.na(df_Leucadia_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Leucadia_2018$OPENPRC, na.rm = TRUE)
df_Leucadia_2018$OPENPRC[is.na(df_Leucadia_2018$OPENPRC)] <- OPEN_AVG

df_Leucadia_2018
```


```{r}
df_Leucadia_2018$text <- clean_tweets(df_Leucadia_2018$text)
df_Leucadia_2018$text <- lemmatize_words(df_Leucadia_2018$text)
```


```{r}
```
```{r}
#Verizon
#Remove columns 
df_Verizon_2018 <- df_Verizon_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Verizon_2018

#Ticker 
df_Verizon_2018$TICKER[is.na(df_Verizon_2018$TICKER)] <- "VZ"

#PRC
PRC_AVG <- mean(df_Verizon_2018$PRC, na.rm = TRUE)
df_Verizon_2018$PRC[is.na(df_Verizon_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Verizon_2018$VOL, na.rm = TRUE)
df_Verizon_2018$VOL[is.na(df_Verizon_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Verizon_2018$BID, na.rm = TRUE)
df_Verizon_2018$BID[is.na(df_Verizon_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Verizon_2018$ASK, na.rm = TRUE)
df_Verizon_2018$ASK[is.na(df_Verizon_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Verizon_2018$OPENPRC, na.rm = TRUE)
df_Verizon_2018$OPENPRC[is.na(df_Verizon_2018$OPENPRC)] <- OPEN_AVG

df_Verizon_2018
```


```{r}
df_Verizon_2018$text <- clean_tweets(df_Verizon_2018$text)
df_Verizon_2018$text <- lemmatize_words(df_Verizon_2018$text)
```


```{r}
```
```{r}
#Western Union
#Remove columns 
df_WU_2016 <- df_WU_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_WU_2016

#Ticker 
df_WU_2016$TICKER[is.na(df_WU_2016$TICKER)] <- "WU"

#PRC
PRC_AVG <- mean(df_WU_2016$PRC, na.rm = TRUE)
df_WU_2016$PRC[is.na(df_WU_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_WU_2016$VOL, na.rm = TRUE)
df_WU_2016$VOL[is.na(df_WU_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_WU_2016$BID, na.rm = TRUE)
df_WU_2016$BID[is.na(df_WU_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_WU_2016$ASK, na.rm = TRUE)
df_WU_2016$ASK[is.na(df_WU_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WU_2016$OPENPRC, na.rm = TRUE)
df_WU_2016$OPENPRC[is.na(df_WU_2016$OPENPRC)] <- OPEN_AVG

df_WU_2016
```


```{r}
df_WU_2016$text <- clean_tweets(df_WU_2016$text)
df_WU_2016$text <- lemmatize_words(df_WU_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_WU_2018 <- df_WU_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_WU_2018

#Ticker 
df_WU_2018$TICKER[is.na(df_WU_2018$TICKER)] <- "WU"

#PRC
PRC_AVG <- mean(df_WU_2018$PRC, na.rm = TRUE)
df_WU_2018$PRC[is.na(df_WU_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_WU_2018$VOL, na.rm = TRUE)
df_WU_2018$VOL[is.na(df_WU_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_WU_2018$BID, na.rm = TRUE)
df_WU_2018$BID[is.na(df_WU_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_WU_2018$ASK, na.rm = TRUE)
df_WU_2018$ASK[is.na(df_WU_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WU_2018$OPENPRC, na.rm = TRUE)
df_WU_2018$OPENPRC[is.na(df_WU_2018$OPENPRC)] <- OPEN_AVG

df_WU_2018
```

```{r}
df_WU_2018$text <- clean_tweets(df_WU_2018$text)
df_WU_2018$text <- lemmatize_words(df_WU_2018$text)
```

```{r}
#Redhat
#Remove columns 
df_RedHat_2016 <- df_RedHat_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_RedHat_2016

#Ticker 
df_RedHat_2016$TICKER[is.na(df_RedHat_2016$TICKER)] <- "RHT"

#PRC
PRC_AVG <- mean(df_RedHat_2016$PRC, na.rm = TRUE)
df_RedHat_2016$PRC[is.na(df_RedHat_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_RedHat_2016$VOL, na.rm = TRUE)
df_RedHat_2016$VOL[is.na(df_RedHat_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_RedHat_2016$BID, na.rm = TRUE)
df_RedHat_2016$BID[is.na(df_RedHat_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_RedHat_2016$ASK, na.rm = TRUE)
df_RedHat_2016$ASK[is.na(df_RedHat_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RedHat_2016$OPENPRC, na.rm = TRUE)
df_RedHat_2016$OPENPRC[is.na(df_RedHat_2016$OPENPRC)] <- OPEN_AVG

df_RedHat_2016
```


```{r}
df_RedHat_2016$text <- clean_tweets(df_RedHat_2016$text)
df_RedHat_2016$text <- lemmatize_words(df_RedHat_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_RedHat_2018 <- df_RedHat_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_RedHat_2018

#Ticker 
df_RedHat_2018$TICKER[is.na(df_RedHat_2018$TICKER)] <- "RHT"

#PRC
PRC_AVG <- mean(df_RedHat_2018$PRC, na.rm = TRUE)
df_RedHat_2018$PRC[is.na(df_RedHat_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_RedHat_2018$VOL, na.rm = TRUE)
df_RedHat_2018$VOL[is.na(df_RedHat_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_RedHat_2018$BID, na.rm = TRUE)
df_RedHat_2018$BID[is.na(df_RedHat_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_RedHat_2018$ASK, na.rm = TRUE)
df_RedHat_2018$ASK[is.na(df_RedHat_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RedHat_2018$OPENPRC, na.rm = TRUE)
df_RedHat_2018$OPENPRC[is.na(df_RedHat_2018$OPENPRC)] <- OPEN_AVG

df_RedHat_2018
```


```{r}
df_RedHat_2018$text <- clean_tweets(df_RedHat_2018$text)
df_RedHat_2018$text <- lemmatize_words(df_RedHat_2018$text)
```


```{r}
```
```{r}
#Amazon
#Remove columns 
df_AMZN_2016 <- df_AMZN_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_AMZN_2016

#Ticker 
df_AMZN_2016$TICKER[is.na(df_AMZN_2016$TICKER)] <- "AMZN"

#PRC
PRC_AVG <- mean(df_AMZN_2016$PRC, na.rm = TRUE)
df_AMZN_2016$PRC[is.na(df_AMZN_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_AMZN_2016$VOL, na.rm = TRUE)
df_AMZN_2016$VOL[is.na(df_AMZN_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_AMZN_2016$BID, na.rm = TRUE)
df_AMZN_2016$BID[is.na(df_AMZN_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMZN_2016$ASK, na.rm = TRUE)
df_AMZN_2016$ASK[is.na(df_AMZN_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMZN_2016$OPENPRC, na.rm = TRUE)
df_AMZN_2016$OPENPRC[is.na(df_AMZN_2016$OPENPRC)] <- OPEN_AVG

df_AMZN_2016
```
```{r}
df_AMZN_2016$text <- clean_tweets(df_AMZN_2016$text)
df_AMZN_2016$text <- lemmatize_words(df_AMZN_2016$text)
```

```{r}
#Remove columns 
df_AMZN_2018 <- df_AMZN_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_AMZN_2018

#Ticker 
df_AMZN_2018$TICKER[is.na(df_AMZN_2018$TICKER)] <- "AMZN"

#PRC
PRC_AVG <- mean(df_AMZN_2018$PRC, na.rm = TRUE)
df_AMZN_2018$PRC[is.na(df_AMZN_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_AMZN_2018$VOL, na.rm = TRUE)
df_AMZN_2018$VOL[is.na(df_AMZN_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_AMZN_2018$BID, na.rm = TRUE)
df_AMZN_2018$BID[is.na(df_AMZN_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMZN_2018$ASK, na.rm = TRUE)
df_AMZN_2018$ASK[is.na(df_AMZN_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMZN_2018$OPENPRC, na.rm = TRUE)
df_AMZN_2018$OPENPRC[is.na(df_AMZN_2018$OPENPRC)] <- OPEN_AVG

df_AMZN_2018
```
```{r}
df_AMZN_2018$text <- clean_tweets(df_AMZN_2018$text)
df_AMZN_2018$text <- lemmatize_words(df_AMZN_2018$text)

```

```{r}
#GE
#Remove columns 
df_GE_2016 <- df_GE_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_GE_2016

#Ticker 
df_GE_2016$TICKER[is.na(df_GE_2016$TICKER)] <- "GE"

#PRC
PRC_AVG <- mean(df_GE_2016$PRC, na.rm = TRUE)
df_GE_2016$PRC[is.na(df_GE_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_GE_2016$VOL, na.rm = TRUE)
df_GE_2016$VOL[is.na(df_GE_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_GE_2016$BID, na.rm = TRUE)
df_GE_2016$BID[is.na(df_GE_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_GE_2016$ASK, na.rm = TRUE)
df_GE_2016$ASK[is.na(df_GE_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GE_2016$OPENPRC, na.rm = TRUE)
df_GE_2016$OPENPRC[is.na(df_GE_2016$OPENPRC)] <- OPEN_AVG

df_GE_2016
```


```{r}
df_GE_2016$text <- clean_tweets(df_GE_2016$text)
df_GE_2016$text <- lemmatize_words(df_GE_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_GE_2017 <- df_GE_2017[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_GE_2017

#Ticker 
df_GE_2017$TICKER[is.na(df_GE_2017$TICKER)] <- "GE"

#PRC
PRC_AVG <- mean(df_GE_2017$PRC, na.rm = TRUE)
df_GE_2017$PRC[is.na(df_GE_2017$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_GE_2017$VOL, na.rm = TRUE)
df_GE_2017$VOL[is.na(df_GE_2017$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_GE_2017$BID, na.rm = TRUE)
df_GE_2017$BID[is.na(df_GE_2017$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_GE_2017$ASK, na.rm = TRUE)
df_GE_2017$ASK[is.na(df_GE_2017$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GE_2017$OPENPRC, na.rm = TRUE)
df_GE_2017$OPENPRC[is.na(df_GE_2017$OPENPRC)] <- OPEN_AVG

df_GE_2017
```
```{r}
df_GE_2017$text <- clean_tweets(df_GE_2017$text)
df_GE_2017$text <- lemmatize_words(df_GE_2017$text)
```

```{r}
#Fiserv
#Remove columns 
df_Fiserv_2016 <- df_Fiserv_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Fiserv_2016

#Ticker 
df_Fiserv_2016$TICKER[is.na(df_Fiserv_2016$TICKER)] <- "FISV"

#PRC
PRC_AVG <- mean(df_Fiserv_2016$PRC, na.rm = TRUE)
df_Fiserv_2016$PRC[is.na(df_Fiserv_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Fiserv_2016$VOL, na.rm = TRUE)
df_Fiserv_2016$VOL[is.na(df_Fiserv_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Fiserv_2016$BID, na.rm = TRUE)
df_Fiserv_2016$BID[is.na(df_Fiserv_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Fiserv_2016$ASK, na.rm = TRUE)
df_Fiserv_2016$ASK[is.na(df_Fiserv_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Fiserv_2016$OPENPRC, na.rm = TRUE)
df_Fiserv_2016$OPENPRC[is.na(df_Fiserv_2016$OPENPRC)] <- OPEN_AVG

df_Fiserv_2016
```
```{r}
df_Fiserv_2016$text <- clean_tweets(df_Fiserv_2016$text)
df_Fiserv_2016$text <- lemmatize_words(df_Fiserv_2016$text)
```

```{r}
#Fiserv
#Remove columns 
df_Fiserv_2018 <- df_Fiserv_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Fiserv_2018

#Ticker 
df_Fiserv_2018$TICKER[is.na(df_Fiserv_2018$TICKER)] <- "FISV"

#PRC
PRC_AVG <- mean(df_Fiserv_2018$PRC, na.rm = TRUE)
df_Fiserv_2018$PRC[is.na(df_Fiserv_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Fiserv_2018$VOL, na.rm = TRUE)
df_Fiserv_2018$VOL[is.na(df_Fiserv_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Fiserv_2018$BID, na.rm = TRUE)
df_Fiserv_2018$BID[is.na(df_Fiserv_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Fiserv_2018$ASK, na.rm = TRUE)
df_Fiserv_2018$ASK[is.na(df_Fiserv_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Fiserv_2018$OPENPRC, na.rm = TRUE)
df_Fiserv_2018$OPENPRC[is.na(df_Fiserv_2018$OPENPRC)] <- OPEN_AVG

df_Fiserv_2018
```
```{r}
df_Fiserv_2018$text <- clean_tweets(df_Fiserv_2018$text)
df_Fiserv_2018$text <- lemmatize_words(df_Fiserv_2018$text)
```

```{r}
#Waste Management
#Remove columns 
df_WM_2018 <- df_WM_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_WM_2018

#Ticker 
df_WM_2018$TICKER[is.na(df_WM_2018$TICKER)] <- "WM"

#PRC
PRC_AVG <- mean(df_WM_2018$PRC, na.rm = TRUE)
df_WM_2018$PRC[is.na(df_WM_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_WM_2018$VOL, na.rm = TRUE)
df_WM_2018$VOL[is.na(df_WM_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_WM_2018$BID, na.rm = TRUE)
df_WM_2018$BID[is.na(df_WM_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_WM_2018$ASK, na.rm = TRUE)
df_WM_2018$ASK[is.na(df_WM_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WM_2018$OPENPRC, na.rm = TRUE)
df_WM_2018$OPENPRC[is.na(df_WM_2018$OPENPRC)] <- OPEN_AVG

df_WM_2018
```
```{r}
df_WM_2018$text <- clean_tweets(df_WM_2018$text)
df_WM_2018$text <- lemmatize_words(df_WM_2018$text)
```

```{r}
#WillsTower
#Remove columns 
df_Wills_2016 <- df_Wills_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Wills_2016

#Ticker 
df_Wills_2016$TICKER[is.na(df_Wills_2016$TICKER)] <- "WLTW"

#PRC
PRC_AVG <- mean(df_Wills_2016$PRC, na.rm = TRUE)
df_Wills_2016$PRC[is.na(df_Wills_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Wills_2016$VOL, na.rm = TRUE)
df_Wills_2016$VOL[is.na(df_Wills_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Wills_2016$BID, na.rm = TRUE)
df_Wills_2016$BID[is.na(df_Wills_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Wills_2016$ASK, na.rm = TRUE)
df_Wills_2016$ASK[is.na(df_Wills_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Wills_2016$OPENPRC, na.rm = TRUE)
df_Wills_2016$OPENPRC[is.na(df_Wills_2016$OPENPRC)] <- OPEN_AVG

df_Wills_2016
```
```{r}
df_Wills_2016$text <- clean_tweets(df_Wills_2016$text)
df_Wills_2016$text <- lemmatize_words(df_Wills_2016$text)
```

```{r}
#Remove columns 
df_Wills_2018 <- df_Wills_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Wills_2018

#Ticker 
df_Wills_2018$TICKER[is.na(df_Wills_2018$TICKER)] <- "WLTW"

#PRC
PRC_AVG <- mean(df_Wills_2018$PRC, na.rm = TRUE)
df_Wills_2018$PRC[is.na(df_Wills_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Wills_2018$VOL, na.rm = TRUE)
df_Wills_2018$VOL[is.na(df_Wills_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Wills_2018$BID, na.rm = TRUE)
df_Wills_2018$BID[is.na(df_Wills_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Wills_2018$ASK, na.rm = TRUE)
df_Wills_2018$ASK[is.na(df_Wills_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Wills_2018$OPENPRC, na.rm = TRUE)
df_Wills_2018$OPENPRC[is.na(df_Wills_2018$OPENPRC)] <- OPEN_AVG

df_Wills_2018
```


```{r}
df_Wills_2018$text <- clean_tweets(df_Wills_2018$text)
df_Wills_2018$text <- lemmatize_words(df_Wills_2018$text)
```


```{r}
```
```{r}
#Tripadvisor
#Remove columns 
df_tripadvisor_2016 <- df_tripadvisor_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_tripadvisor_2016

#Ticker 
df_tripadvisor_2016$TICKER[is.na(df_tripadvisor_2016$TICKER)] <- "TRIP"

#PRC
PRC_AVG <- mean(df_tripadvisor_2016$PRC, na.rm = TRUE)
df_tripadvisor_2016$PRC[is.na(df_tripadvisor_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_tripadvisor_2016$VOL, na.rm = TRUE)
df_tripadvisor_2016$VOL[is.na(df_tripadvisor_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_tripadvisor_2016$BID, na.rm = TRUE)
df_tripadvisor_2016$BID[is.na(df_tripadvisor_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_tripadvisor_2016$ASK, na.rm = TRUE)
df_tripadvisor_2016$ASK[is.na(df_tripadvisor_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_tripadvisor_2016$OPENPRC, na.rm = TRUE)
df_tripadvisor_2016$OPENPRC[is.na(df_tripadvisor_2016$OPENPRC)] <- OPEN_AVG

df_tripadvisor_2016
```
```{r}
df_tripadvisor_2016$text <- clean_tweets(df_tripadvisor_2016$text)
df_tripadvisor_2016$text <- lemmatize_words(df_tripadvisor_2016$text)
```

```{r}
#Remove columns 
df_tripadvisor_2018 <- df_tripadvisor_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_tripadvisor_2018

#Ticker 
df_tripadvisor_2018$TICKER[is.na(df_tripadvisor_2018$TICKER)] <- "TRIP"

#PRC
PRC_AVG <- mean(df_tripadvisor_2018$PRC, na.rm = TRUE)
df_tripadvisor_2018$PRC[is.na(df_tripadvisor_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_tripadvisor_2018$VOL, na.rm = TRUE)
df_tripadvisor_2018$VOL[is.na(df_tripadvisor_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_tripadvisor_2018$BID, na.rm = TRUE)
df_tripadvisor_2018$BID[is.na(df_tripadvisor_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_tripadvisor_2018$ASK, na.rm = TRUE)
df_tripadvisor_2018$ASK[is.na(df_tripadvisor_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_tripadvisor_2018$OPENPRC, na.rm = TRUE)
df_tripadvisor_2018$OPENPRC[is.na(df_tripadvisor_2018$OPENPRC)] <- OPEN_AVG

df_tripadvisor_2018
```


```{r}
df_tripadvisor_2018$text <- clean_tweets(df_tripadvisor_2018$text)
df_tripadvisor_2018$text <- lemmatize_words(df_tripadvisor_2018$text)
```


```{r}
```
```{r}
#DavitaKent
#Remove columns 
df_DavitaK_2016 <- df_DavitaK_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_DavitaK_2016

#Ticker 
df_DavitaK_2016$TICKER[is.na(df_DavitaK_2016$TICKER)] <- "DVA"

#PRC
PRC_AVG <- mean(df_DavitaK_2016$PRC, na.rm = TRUE)
df_DavitaK_2016$PRC[is.na(df_DavitaK_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_DavitaK_2016$VOL, na.rm = TRUE)
df_DavitaK_2016$VOL[is.na(df_DavitaK_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_DavitaK_2016$BID, na.rm = TRUE)
df_DavitaK_2016$BID[is.na(df_DavitaK_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_DavitaK_2016$ASK, na.rm = TRUE)
df_DavitaK_2016$ASK[is.na(df_DavitaK_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_DavitaK_2016$OPENPRC, na.rm = TRUE)
df_DavitaK_2016$OPENPRC[is.na(df_DavitaK_2016$OPENPRC)] <- OPEN_AVG

df_DavitaK_2016
```


```{r}
df_DavitaK_2016$text <- clean_tweets(df_DavitaK_2016$text)
df_DavitaK_2016$text <- lemmatize_words(df_DavitaK_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_DavitaK_2018 <- df_DavitaK_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_DavitaK_2018

#Ticker 
df_DavitaK_2018$TICKER[is.na(df_DavitaK_2018$TICKER)] <- "DVA"

#PRC
PRC_AVG <- mean(df_DavitaK_2018$PRC, na.rm = TRUE)
df_DavitaK_2018$PRC[is.na(df_DavitaK_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_DavitaK_2018$VOL, na.rm = TRUE)
df_DavitaK_2018$VOL[is.na(df_DavitaK_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_DavitaK_2018$BID, na.rm = TRUE)
df_DavitaK_2018$BID[is.na(df_DavitaK_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_DavitaK_2018$ASK, na.rm = TRUE)
df_DavitaK_2018$ASK[is.na(df_DavitaK_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_DavitaK_2018$OPENPRC, na.rm = TRUE)
df_DavitaK_2018$OPENPRC[is.na(df_DavitaK_2018$OPENPRC)] <- OPEN_AVG

df_DavitaK_2018
```
```{r}
df_DavitaK_2018$text <- clean_tweets(df_DavitaK_2018$text)
df_DavitaK_2018$text <- lemmatize_words(df_DavitaK_2018$text)
```

```{r}
#Starbucks 
#Remove columns 
df_Starbucks_2018 <- df_Starbucks_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Starbucks_2018

#Ticker 
df_Starbucks_2018$TICKER[is.na(df_Starbucks_2018$TICKER)] <- "SBUX"

#PRC
PRC_AVG <- mean(df_Starbucks_2018$PRC, na.rm = TRUE)
df_Starbucks_2018$PRC[is.na(df_Starbucks_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Starbucks_2018$VOL, na.rm = TRUE)
df_Starbucks_2018$VOL[is.na(df_Starbucks_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Starbucks_2018$BID, na.rm = TRUE)
df_Starbucks_2018$BID[is.na(df_Starbucks_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Starbucks_2018$ASK, na.rm = TRUE)
df_Starbucks_2018$ASK[is.na(df_Starbucks_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Starbucks_2018$OPENPRC, na.rm = TRUE)
df_Starbucks_2018$OPENPRC[is.na(df_Starbucks_2018$OPENPRC)] <- OPEN_AVG

df_Starbucks_2018
```


```{r}
df_Starbucks_2018$text <- clean_tweets(df_Starbucks_2018$text)
df_Starbucks_2018$text <- lemmatize_words(df_Starbucks_2018$text)
```


```{r}
```
```{r}
#McCormick
#Remove columns 
df_McCormick_2018 <- df_McCormick_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_McCormick_2018

#Ticker 
df_McCormick_2018$TICKER[is.na(df_McCormick_2018$TICKER)] <- "MKC"

#PRC
PRC_AVG <- mean(df_McCormick_2018$PRC, na.rm = TRUE)
df_McCormick_2018$PRC[is.na(df_McCormick_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_McCormick_2018$VOL, na.rm = TRUE)
df_McCormick_2018$VOL[is.na(df_McCormick_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_McCormick_2018$BID, na.rm = TRUE)
df_McCormick_2018$BID[is.na(df_McCormick_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_McCormick_2018$ASK, na.rm = TRUE)
df_McCormick_2018$ASK[is.na(df_McCormick_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_McCormick_2018$OPENPRC, na.rm = TRUE)
df_McCormick_2018$OPENPRC[is.na(df_McCormick_2018$OPENPRC)] <- OPEN_AVG

df_McCormick_2018
```
```{r}
df_McCormick_2018$text <- clean_tweets(df_McCormick_2018$text)
df_McCormick_2018$text <- lemmatize_words(df_McCormick_2018$text)
```

```{r}
#IHS Markit 
#Remove columns 
df_IHS_2018 <- df_IHS_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_IHS_2018

#Ticker 
df_IHS_2018$TICKER[is.na(df_IHS_2018$TICKER)] <- "TCX"

#PRC
PRC_AVG <- mean(df_IHS_2018$PRC, na.rm = TRUE)
df_IHS_2018$PRC[is.na(df_IHS_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_IHS_2018$VOL, na.rm = TRUE)
df_IHS_2018$VOL[is.na(df_IHS_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_IHS_2018$BID, na.rm = TRUE)
df_IHS_2018$BID[is.na(df_IHS_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_IHS_2018$ASK, na.rm = TRUE)
df_IHS_2018$ASK[is.na(df_IHS_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_IHS_2018$OPENPRC, na.rm = TRUE)
df_IHS_2018$OPENPRC[is.na(df_IHS_2018$OPENPRC)] <- OPEN_AVG

df_IHS_2018
```
```{r}
df_IHS_2018$text <- clean_tweets(df_IHS_2018$text)
df_IHS_2018$text <- lemmatize_words(df_IHS_2018$text)
```

```{r}
#AMD 
#Remove columns 
df_AMD_2018 <- df_AMD_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_AMD_2018

#Ticker 
df_AMD_2018$TICKER[is.na(df_AMD_2018$TICKER)] <- "AMD"

#PRC
PRC_AVG <- mean(df_AMD_2018$PRC, na.rm = TRUE)
df_AMD_2018$PRC[is.na(df_AMD_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_AMD_2018$VOL, na.rm = TRUE)
df_AMD_2018$VOL[is.na(df_AMD_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_AMD_2018$BID, na.rm = TRUE)
df_AMD_2018$BID[is.na(df_AMD_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMD_2018$ASK, na.rm = TRUE)
df_AMD_2018$ASK[is.na(df_AMD_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMD_2018$OPENPRC, na.rm = TRUE)
df_AMD_2018$OPENPRC[is.na(df_AMD_2018$OPENPRC)] <- OPEN_AVG

df_AMD_2018
```


```{r}
df_AMD_2018$text <- clean_tweets(df_AMD_2018$text)
df_AMD_2018$text <- lemmatize_words(df_AMD_2018$text)
```


```{r}
```
```{r}
#ResMed
#Remove columns 
df_ResMed_2016 <- df_ResMed_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_ResMed_2016

#Ticker 
df_ResMed_2016$TICKER[is.na(df_ResMed_2016$TICKER)] <- "RMD"

#PRC
PRC_AVG <- mean(df_ResMed_2016$PRC, na.rm = TRUE)
df_ResMed_2016$PRC[is.na(df_ResMed_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_ResMed_2016$VOL, na.rm = TRUE)
df_ResMed_2016$VOL[is.na(df_ResMed_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_ResMed_2016$BID, na.rm = TRUE)
df_ResMed_2016$BID[is.na(df_ResMed_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_ResMed_2016$ASK, na.rm = TRUE)
df_ResMed_2016$ASK[is.na(df_ResMed_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_ResMed_2016$OPENPRC, na.rm = TRUE)
df_ResMed_2016$OPENPRC[is.na(df_ResMed_2016$OPENPRC)] <- OPEN_AVG

df_ResMed_2016
```


```{r}
df_ResMed_2016$text <- clean_tweets(df_ResMed_2016$text)
df_ResMed_2016$text <- lemmatize_words(df_ResMed_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_ResMed_2018 <- df_ResMed_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_ResMed_2018

#Ticker 
df_ResMed_2018$TICKER[is.na(df_ResMed_2018$TICKER)] <- "RMD"

#PRC
PRC_AVG <- mean(df_ResMed_2018$PRC, na.rm = TRUE)
df_ResMed_2018$PRC[is.na(df_ResMed_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_ResMed_2018$VOL, na.rm = TRUE)
df_ResMed_2018$VOL[is.na(df_ResMed_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_ResMed_2018$BID, na.rm = TRUE)
df_ResMed_2018$BID[is.na(df_ResMed_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_ResMed_2018$ASK, na.rm = TRUE)
df_ResMed_2018$ASK[is.na(df_ResMed_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_ResMed_2018$OPENPRC, na.rm = TRUE)
df_ResMed_2018$OPENPRC[is.na(df_ResMed_2018$OPENPRC)] <- OPEN_AVG

df_ResMed_2018
```
```{r}
df_ResMed_2018$text <- clean_tweets(df_ResMed_2018$text)
df_ResMed_2018$text <- lemmatize_words(df_ResMed_2018$text)
```

```{r}
#CA
#Remove columns 
df_CA_2016 <- df_CA_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_CA_2016

#Ticker 
df_CA_2016$TICKER[is.na(df_CA_2016$TICKER)] <- "CA"

#PRC
PRC_AVG <- mean(df_CA_2016$PRC, na.rm = TRUE)
df_CA_2016$PRC[is.na(df_CA_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_CA_2016$VOL, na.rm = TRUE)
df_CA_2016$VOL[is.na(df_CA_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_CA_2016$BID, na.rm = TRUE)
df_CA_2016$BID[is.na(df_CA_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_CA_2016$ASK, na.rm = TRUE)
df_CA_2016$ASK[is.na(df_CA_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CA_2016$OPENPRC, na.rm = TRUE)
df_CA_2016$OPENPRC[is.na(df_CA_2016$OPENPRC)] <- OPEN_AVG

df_CA_2016
```
```{r}
df_CA_2016$text <- clean_tweets(df_CA_2016$text)
df_CA_2016$text <- lemmatize_words(df_CA_2016$text)
```


```{r}
#Remove columns 
df_CA_2018 <- df_CA_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_CA_2018

#Ticker 
df_CA_2018$TICKER[is.na(df_CA_2018$TICKER)] <- "CA"

#PRC
PRC_AVG <- mean(df_CA_2018$PRC, na.rm = TRUE)
df_CA_2018$PRC[is.na(df_CA_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_CA_2018$VOL, na.rm = TRUE)
df_CA_2018$VOL[is.na(df_CA_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_CA_2018$BID, na.rm = TRUE)
df_CA_2018$BID[is.na(df_CA_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_CA_2018$ASK, na.rm = TRUE)
df_CA_2018$ASK[is.na(df_CA_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_CA_2018$OPENPRC, na.rm = TRUE)
df_CA_2018$OPENPRC[is.na(df_CA_2018$OPENPRC)] <- OPEN_AVG

df_CA_2018
```


```{r}
df_CA_2018$text <- clean_tweets(df_CA_2018$text)
df_CA_2018$text <- lemmatize_words(df_CA_2018$text)
```


```{r}
```
```{r}
#GM 
#Remove columns 
df_GM_2016 <- df_GM_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_GM_2016

#Ticker 
df_GM_2016$TICKER[is.na(df_GM_2016$TICKER)] <- "GM"

#PRC
PRC_AVG <- mean(df_GM_2016$PRC, na.rm = TRUE)
df_GM_2016$PRC[is.na(df_GM_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_GM_2016$VOL, na.rm = TRUE)
df_GM_2016$VOL[is.na(df_GM_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_GM_2016$BID, na.rm = TRUE)
df_GM_2016$BID[is.na(df_GM_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_GM_2016$ASK, na.rm = TRUE)
df_GM_2016$ASK[is.na(df_GM_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GM_2016$OPENPRC, na.rm = TRUE)
df_GM_2016$OPENPRC[is.na(df_GM_2016$OPENPRC)] <- OPEN_AVG

df_GM_2016
```


```{r}
df_GM_2016$text <- clean_tweets(df_GM_2016$text)
df_GM_2016$text <- lemmatize_words(df_GM_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_GM_2018 <- df_GM_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_GM_2018

#Ticker 
df_GM_2018$TICKER[is.na(df_GM_2018$TICKER)] <- "GM"

#PRC
PRC_AVG <- mean(df_GM_2018$PRC, na.rm = TRUE)
df_GM_2018$PRC[is.na(df_GM_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_GM_2018$VOL, na.rm = TRUE)
df_GM_2018$VOL[is.na(df_GM_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_GM_2018$BID, na.rm = TRUE)
df_GM_2018$BID[is.na(df_GM_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_GM_2018$ASK, na.rm = TRUE)
df_GM_2018$ASK[is.na(df_GM_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GM_2018$OPENPRC, na.rm = TRUE)
df_GM_2018$OPENPRC[is.na(df_GM_2018$OPENPRC)] <- OPEN_AVG

df_GM_2018
```


```{r}
df_GM_2018$text <- clean_tweets(df_GM_2018$text)
df_GM_2018$text <- lemmatize_words(df_GM_2018$text)
```
```{r}
#Aetna
#Remove columns 
df_Aetna_2016 <- df_Aetna_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Aetna_2016

#Ticker 
df_Aetna_2016$TICKER[is.na(df_Aetna_2016$TICKER)] <- "AET"

#PRC
PRC_AVG <- mean(df_Aetna_2016$PRC, na.rm = TRUE)
df_Aetna_2016$PRC[is.na(df_Aetna_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Aetna_2016$VOL, na.rm = TRUE)
df_Aetna_2016$VOL[is.na(df_Aetna_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Aetna_2016$BID, na.rm = TRUE)
df_Aetna_2016$BID[is.na(df_Aetna_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Aetna_2016$ASK, na.rm = TRUE)
df_Aetna_2016$ASK[is.na(df_Aetna_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Aetna_2016$OPENPRC, na.rm = TRUE)
df_Aetna_2016$OPENPRC[is.na(df_Aetna_2016$OPENPRC)] <- OPEN_AVG

df_Aetna_2016
```
```{r}
df_Aetna_2016$text <- clean_tweets(df_Aetna_2016$text)
df_Aetna_2016$text <- lemmatize_words(df_Aetna_2016$text)
```

```{r}
#Remove columns 
df_Aetna_2018 <- df_Aetna_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Aetna_2018

#Ticker 
df_Aetna_2018$TICKER[is.na(df_Aetna_2018$TICKER)] <- "AET"

#PRC
PRC_AVG <- mean(df_Aetna_2018$PRC, na.rm = TRUE)
df_Aetna_2018$PRC[is.na(df_Aetna_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Aetna_2018$VOL, na.rm = TRUE)
df_Aetna_2018$VOL[is.na(df_Aetna_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Aetna_2018$BID, na.rm = TRUE)
df_Aetna_2018$BID[is.na(df_Aetna_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Aetna_2018$ASK, na.rm = TRUE)
df_Aetna_2018$ASK[is.na(df_Aetna_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Aetna_2018$OPENPRC, na.rm = TRUE)
df_Aetna_2018$OPENPRC[is.na(df_Aetna_2018$OPENPRC)] <- OPEN_AVG

df_Aetna_2018
```
```{r}
df_Aetna_2018$text <- clean_tweets(df_Aetna_2018$text)
df_Aetna_2018$text <- lemmatize_words(df_Aetna_2018$text)
```

```{r}
#NRG
#Remove columns 
df_NRG_2016 <- df_NRG_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_NRG_2016

#Ticker 
df_NRG_2016$TICKER[is.na(df_NRG_2016$TICKER)] <- "NRG"

#PRC
PRC_AVG <- mean(df_NRG_2016$PRC, na.rm = TRUE)
df_NRG_2016$PRC[is.na(df_NRG_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_NRG_2016$VOL, na.rm = TRUE)
df_NRG_2016$VOL[is.na(df_NRG_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_NRG_2016$BID, na.rm = TRUE)
df_NRG_2016$BID[is.na(df_NRG_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_NRG_2016$ASK, na.rm = TRUE)
df_NRG_2016$ASK[is.na(df_NRG_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_NRG_2016$OPENPRC, na.rm = TRUE)
df_NRG_2016$OPENPRC[is.na(df_NRG_2016$OPENPRC)] <- OPEN_AVG

df_NRG_2016
```


```{r}
df_NRG_2016$text <- clean_tweets(df_NRG_2016$text)
df_NRG_2016$text <- lemmatize_words(df_NRG_2016$text)
```


```{r}
```
```{r}
#Remove columns 
df_NRG_2018 <- df_NRG_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_NRG_2018

#Ticker 
df_NRG_2018$TICKER[is.na(df_NRG_2018$TICKER)] <- "NRG"

#PRC
PRC_AVG <- mean(df_NRG_2018$PRC, na.rm = TRUE)
df_NRG_2018$PRC[is.na(df_NRG_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_NRG_2018$VOL, na.rm = TRUE)
df_NRG_2018$VOL[is.na(df_NRG_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_NRG_2018$BID, na.rm = TRUE)
df_NRG_2018$BID[is.na(df_NRG_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_NRG_2018$ASK, na.rm = TRUE)
df_NRG_2018$ASK[is.na(df_NRG_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_NRG_2018$OPENPRC, na.rm = TRUE)
df_NRG_2018$OPENPRC[is.na(df_NRG_2018$OPENPRC)] <- OPEN_AVG

df_NRG_2018
```


```{r}
df_NRG_2018$text <- clean_tweets(df_NRG_2018$text)
df_NRG_2018$text <- lemmatize_words(df_NRG_2018$text)
```


```{r}
```
```{r}
#Medtronic
#Remove columns 
df_Medtronic_2016 <- df_Medtronic_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Medtronic_2016

#Ticker 
df_Medtronic_2016$TICKER[is.na(df_Medtronic_2016$TICKER)] <- "MDT"

#PRC
PRC_AVG <- mean(df_Medtronic_2016$PRC, na.rm = TRUE)
df_Medtronic_2016$PRC[is.na(df_Medtronic_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Medtronic_2016$VOL, na.rm = TRUE)
df_Medtronic_2016$VOL[is.na(df_Medtronic_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Medtronic_2016$BID, na.rm = TRUE)
df_Medtronic_2016$BID[is.na(df_Medtronic_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Medtronic_2016$ASK, na.rm = TRUE)
df_Medtronic_2016$ASK[is.na(df_Medtronic_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Medtronic_2016$OPENPRC, na.rm = TRUE)
df_Medtronic_2016$OPENPRC[is.na(df_Medtronic_2016$OPENPRC)] <- OPEN_AVG

df_Medtronic_2016
```
```{r}
df_Medtronic_2016$text <- clean_tweets(df_Medtronic_2016$text)
df_Medtronic_2016$text <- lemmatize_words(df_Medtronic_2016$text)
```

```{r}
#Remove columns 
df_Medtronic_2018 <- df_Medtronic_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Medtronic_2018

#Ticker 
df_Medtronic_2018$TICKER[is.na(df_Medtronic_2018$TICKER)] <- "MDT"

#PRC
PRC_AVG <- mean(df_Medtronic_2018$PRC, na.rm = TRUE)
df_Medtronic_2018$PRC[is.na(df_Medtronic_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Medtronic_2018$VOL, na.rm = TRUE)
df_Medtronic_2018$VOL[is.na(df_Medtronic_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Medtronic_2018$BID, na.rm = TRUE)
df_Medtronic_2018$BID[is.na(df_Medtronic_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Medtronic_2018$ASK, na.rm = TRUE)
df_Medtronic_2018$ASK[is.na(df_Medtronic_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Medtronic_2018$OPENPRC, na.rm = TRUE)
df_Medtronic_2018$OPENPRC[is.na(df_Medtronic_2018$OPENPRC)] <- OPEN_AVG

df_Medtronic_2018
```
```{r}
df_Medtronic_2018$text <- clean_tweets(df_Medtronic_2018$text)
df_Medtronic_2018$text <- lemmatize_words(df_Medtronic_2018$text)
```

```{r}
#Juniper
#Remove columns 
df_Juniper_2016 <- df_Juniper_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Juniper_2016

#Ticker 
df_Juniper_2016$TICKER[is.na(df_Juniper_2016$TICKER)] <- "JNPR"

#PRC
PRC_AVG <- mean(df_Juniper_2016$PRC, na.rm = TRUE)
df_Juniper_2016$PRC[is.na(df_Juniper_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Juniper_2016$VOL, na.rm = TRUE)
df_Juniper_2016$VOL[is.na(df_Juniper_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_2016$BID, na.rm = TRUE)
df_Juniper_2016$BID[is.na(df_Juniper_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_2016$ASK, na.rm = TRUE)
df_Juniper_2016$ASK[is.na(df_Juniper_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_2016$OPENPRC, na.rm = TRUE)
df_Juniper_2016$OPENPRC[is.na(df_Juniper_2016$OPENPRC)] <- OPEN_AVG

df_Juniper_2016
```
```{r}
df_Juniper_2016$text <- clean_tweets(df_Juniper_2016$text)
df_Juniper_2016$text <- lemmatize_words(df_Juniper_2016$text)
```

```{r}
#Remove columns 
df_Juniper_2018 <- df_Juniper_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Juniper_2018

#Ticker 
df_Juniper_2018$TICKER[is.na(df_Juniper_2018$TICKER)] <- "JNPR"

#PRC
PRC_AVG <- mean(df_Juniper_2018$PRC, na.rm = TRUE)
df_Juniper_2018$PRC[is.na(df_Juniper_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Juniper_2018$VOL, na.rm = TRUE)
df_Juniper_2018$VOL[is.na(df_Juniper_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_2018$BID, na.rm = TRUE)
df_Juniper_2018$BID[is.na(df_Juniper_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_2018$ASK, na.rm = TRUE)
df_Juniper_2018$ASK[is.na(df_Juniper_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_2018$OPENPRC, na.rm = TRUE)
df_Juniper_2018$OPENPRC[is.na(df_Juniper_2018$OPENPRC)] <- OPEN_AVG

df_Juniper_2018
```
```{r}
df_Juniper_2018$text <- clean_tweets(df_Juniper_2018$text)
df_Juniper_2018$text <- lemmatize_words(df_Juniper_2018$text)
```

```{r}
#AetnaR
df_AetnaR_2018 <- df_AetnaR_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_AetnaR_2018

#Ticker 
df_AetnaR_2018$TICKER[is.na(df_AetnaR_2018$TICKER)] <- "AET"

#PRC
PRC_AVG <- mean(df_AetnaR_2018$PRC, na.rm = TRUE)
df_AetnaR_2018$PRC[is.na(df_AetnaR_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_AetnaR_2018$VOL, na.rm = TRUE)
df_AetnaR_2018$VOL[is.na(df_AetnaR_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_AetnaR_2018$BID, na.rm = TRUE)
df_AetnaR_2018$BID[is.na(df_AetnaR_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_AetnaR_2018$ASK, na.rm = TRUE)
df_AetnaR_2018$ASK[is.na(df_AetnaR_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AetnaR_2018$OPENPRC, na.rm = TRUE)
df_AetnaR_2018$OPENPRC[is.na(df_AetnaR_2018$OPENPRC)] <- OPEN_AVG

df_AetnaR_2018
```


```{r}
df_AetnaR_2018$text <- clean_tweets(df_AetnaR_2018$text)
df_AetnaR_2018$text <- lemmatize_words(df_AetnaR_2018$text)
```
```{r}
#Netflix
df_Netflix_2016 <- df_Netflix_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Netflix_2016

#Ticker 
df_Netflix_2016$TICKER[is.na(df_Netflix_2016$TICKER)] <- "NFLX"

#PRC
PRC_AVG <- mean(df_Netflix_2016$PRC, na.rm = TRUE)
df_Netflix_2016$PRC[is.na(df_Netflix_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Netflix_2016$VOL, na.rm = TRUE)
df_Netflix_2016$VOL[is.na(df_Netflix_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Netflix_2016$BID, na.rm = TRUE)
df_Netflix_2016$BID[is.na(df_Netflix_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Netflix_2016$ASK, na.rm = TRUE)
df_Netflix_2016$ASK[is.na(df_Netflix_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Netflix_2016$OPENPRC, na.rm = TRUE)
df_Netflix_2016$OPENPRC[is.na(df_Netflix_2016$OPENPRC)] <- OPEN_AVG

df_Netflix_2016
```
```{r}
df_Netflix_2016$text <- clean_tweets(df_Netflix_2016$text)
df_Netflix_2016$text <- lemmatize_words(df_Netflix_2016$text)
```

```{r}
#Disney
df_Disney_2018 <- df_Disney_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Disney_2018

#Ticker 
df_Disney_2018$TICKER[is.na(df_Disney_2018$TICKER)] <- "DIS"

#PRC
PRC_AVG <- mean(df_Disney_2018$PRC, na.rm = TRUE)
df_Disney_2018$PRC[is.na(df_Disney_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Disney_2018$VOL, na.rm = TRUE)
df_Disney_2018$VOL[is.na(df_Disney_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Disney_2018$BID, na.rm = TRUE)
df_Disney_2018$BID[is.na(df_Disney_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Disney_2018$ASK, na.rm = TRUE)
df_Disney_2018$ASK[is.na(df_Disney_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Disney_2018$OPENPRC, na.rm = TRUE)
df_Disney_2018$OPENPRC[is.na(df_Disney_2018$OPENPRC)] <- OPEN_AVG

df_Disney_2018
```


```{r}
df_Disney_2018$text <- clean_tweets(df_Disney_2018$text)
df_Disney_2018$text <- lemmatize_words(df_Disney_2018$text)
```
```{r}
#Fox 
df_Fox_2015 <- df_Fox_2015[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Fox_2015

#Ticker 
df_Fox_2015$TICKER[is.na(df_Fox_2015$TICKER)] <- "FOX"

#PRC
PRC_AVG <- mean(df_Fox_2015$PRC, na.rm = TRUE)
df_Fox_2015$PRC[is.na(df_Fox_2015$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Fox_2015$VOL, na.rm = TRUE)
df_Fox_2015$VOL[is.na(df_Fox_2015$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Fox_2015$BID, na.rm = TRUE)
df_Fox_2015$BID[is.na(df_Fox_2015$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Fox_2015$ASK, na.rm = TRUE)
df_Fox_2015$ASK[is.na(df_Fox_2015$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Fox_2015$OPENPRC, na.rm = TRUE)
df_Fox_2015$OPENPRC[is.na(df_Fox_2015$OPENPRC)] <- OPEN_AVG

df_Fox_2015
```


```{r}
df_Fox_2015$text <- clean_tweets(df_Fox_2015$text)
df_Fox_2015$text <- lemmatize_words(df_Fox_2015$text)
```

```{r}
#Microsoft 
df_Microsoft_2016 <- df_Microsoft_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Microsoft_2016

#Ticker 
df_Microsoft_2016$TICKER[is.na(df_Microsoft_2016$TICKER)] <- "MSFT"

#PRC
PRC_AVG <- mean(df_Microsoft_2016$PRC, na.rm = TRUE)
df_Microsoft_2016$PRC[is.na(df_Microsoft_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Microsoft_2016$VOL, na.rm = TRUE)
df_Microsoft_2016$VOL[is.na(df_Microsoft_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Microsoft_2016$BID, na.rm = TRUE)
df_Microsoft_2016$BID[is.na(df_Microsoft_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Microsoft_2016$ASK, na.rm = TRUE)
df_Microsoft_2016$ASK[is.na(df_Microsoft_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Microsoft_2016$OPENPRC, na.rm = TRUE)
df_Microsoft_2016$OPENPRC[is.na(df_Microsoft_2016$OPENPRC)] <- OPEN_AVG

df_Microsoft_2016
```


```{r}
df_Microsoft_2016$text <- clean_tweets(df_Microsoft_2016$text)
df_Microsoft_2016$text <- lemmatize_words(df_Microsoft_2016$text)
```
```{r}
df_Microsoft_2018 <- df_Microsoft_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Microsoft_2018

#Ticker 
df_Microsoft_2018$TICKER[is.na(df_Microsoft_2018$TICKER)] <- "MSFT"

#PRC
PRC_AVG <- mean(df_Microsoft_2018$PRC, na.rm = TRUE)
df_Microsoft_2018$PRC[is.na(df_Microsoft_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Microsoft_2018$VOL, na.rm = TRUE)
df_Microsoft_2018$VOL[is.na(df_Microsoft_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Microsoft_2018$BID, na.rm = TRUE)
df_Microsoft_2018$BID[is.na(df_Microsoft_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Microsoft_2018$ASK, na.rm = TRUE)
df_Microsoft_2018$ASK[is.na(df_Microsoft_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Microsoft_2018$OPENPRC, na.rm = TRUE)
df_Microsoft_2018$OPENPRC[is.na(df_Microsoft_2018$OPENPRC)] <- OPEN_AVG

df_Microsoft_2018
```


```{r}
df_Microsoft_2018$text <- clean_tweets(df_Microsoft_2018$text)
df_Microsoft_2018$text <- lemmatize_words(df_Microsoft_2018$text)
```
```{r}
#Juniper_Shaygan
df_Juniper_S_2014 <- df_Juniper_S_2014[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Juniper_S_2014

#Ticker 
df_Juniper_S_2014$TICKER[is.na(df_Juniper_S_2014$TICKER)] <- "JNPR"

#PRC
PRC_AVG <- mean(df_Juniper_S_2014$PRC, na.rm = TRUE)
df_Juniper_S_2014$PRC[is.na(df_Juniper_S_2014$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Juniper_S_2014$VOL, na.rm = TRUE)
df_Juniper_S_2014$VOL[is.na(df_Juniper_S_2014$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_S_2014$BID, na.rm = TRUE)
df_Juniper_S_2014$BID[is.na(df_Juniper_S_2014$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_S_2014$ASK, na.rm = TRUE)
df_Juniper_S_2014$ASK[is.na(df_Juniper_S_2014$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_S_2014$OPENPRC, na.rm = TRUE)
df_Juniper_S_2014$OPENPRC[is.na(df_Juniper_S_2014$OPENPRC)] <- OPEN_AVG

df_Juniper_S_2014
```


```{r}
df_Juniper_S_2014$text <- clean_tweets(df_Juniper_S_2014$text)
df_Juniper_S_2014$text <- lemmatize_words(df_Juniper_S_2014$text)
```
```{r}
df_Juniper_S_2016 <- df_Juniper_S_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Juniper_S_2016

#Ticker 
df_Juniper_S_2016$TICKER[is.na(df_Juniper_S_2016$TICKER)] <- "JNPR"

#PRC
PRC_AVG <- mean(df_Juniper_S_2016$PRC, na.rm = TRUE)
df_Juniper_S_2016$PRC[is.na(df_Juniper_S_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Juniper_S_2016$VOL, na.rm = TRUE)
df_Juniper_S_2016$VOL[is.na(df_Juniper_S_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Juniper_S_2016$BID, na.rm = TRUE)
df_Juniper_S_2016$BID[is.na(df_Juniper_S_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Juniper_S_2016$ASK, na.rm = TRUE)
df_Juniper_S_2016$ASK[is.na(df_Juniper_S_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Juniper_S_2016$OPENPRC, na.rm = TRUE)
df_Juniper_S_2016$OPENPRC[is.na(df_Juniper_S_2016$OPENPRC)] <- OPEN_AVG

df_Juniper_S_2016
```
```{r}
df_Juniper_S_2016$text <- clean_tweets(df_Juniper_S_2016$text)
df_Juniper_S_2016$text <- lemmatize_words(df_Juniper_S_2016$text)
```

```{r}
#Synchrony Financial 
df_SYFM_2016 <- df_SYFM_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_SYFM_2016

#Ticker 
df_SYFM_2016$TICKER[is.na(df_SYFM_2016$TICKER)] <- "SYF"

#PRC
PRC_AVG <- mean(df_SYFM_2016$PRC, na.rm = TRUE)
df_SYFM_2016$PRC[is.na(df_SYFM_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_SYFM_2016$VOL, na.rm = TRUE)
df_SYFM_2016$VOL[is.na(df_SYFM_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_SYFM_2016$BID, na.rm = TRUE)
df_SYFM_2016$BID[is.na(df_SYFM_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_SYFM_2016$ASK, na.rm = TRUE)
df_SYFM_2016$ASK[is.na(df_SYFM_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_SYFM_2016$OPENPRC, na.rm = TRUE)
df_SYFM_2016$OPENPRC[is.na(df_SYFM_2016$OPENPRC)] <- OPEN_AVG

df_SYFM_2016
```


```{r}

df_SYFM_2016$text <- clean_tweets(df_SYFM_2016$text)
df_SYFM_2016$text <- lemmatize_words(df_SYFM_2016$text)
```
```{r}
df_SYFM_2018 <- df_SYFM_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_SYFM_2018

#Ticker 
df_SYFM_2018$TICKER[is.na(df_SYFM_2018$TICKER)] <- "SYF"

#PRC
PRC_AVG <- mean(df_SYFM_2018$PRC, na.rm = TRUE)
df_SYFM_2018$PRC[is.na(df_SYFM_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_SYFM_2018$VOL, na.rm = TRUE)
df_SYFM_2018$VOL[is.na(df_SYFM_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_SYFM_2018$BID, na.rm = TRUE)
df_SYFM_2018$BID[is.na(df_SYFM_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_SYFM_2018$ASK, na.rm = TRUE)
df_SYFM_2018$ASK[is.na(df_SYFM_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_SYFM_2018$OPENPRC, na.rm = TRUE)
df_SYFM_2018$OPENPRC[is.na(df_SYFM_2018$OPENPRC)] <- OPEN_AVG

df_SYFM_2018
```


```{r}
df_SYFM_2018$text <- clean_tweets(df_SYFM_2018$text)
df_SYFM_2018$text <- lemmatize_words(df_SYFM_2018$text)
```
```{r}
#Southern company 
df_Southern_2016 <- df_Southern_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Southern_2016

#Ticker 
df_Southern_2016$TICKER[is.na(df_Southern_2016$TICKER)] <- "SO"

#PRC
PRC_AVG <- mean(df_Southern_2016$PRC, na.rm = TRUE)
df_Southern_2016$PRC[is.na(df_Southern_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Southern_2016$VOL, na.rm = TRUE)
df_Southern_2016$VOL[is.na(df_Southern_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Southern_2016$BID, na.rm = TRUE)
df_Southern_2016$BID[is.na(df_Southern_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southern_2016$ASK, na.rm = TRUE)
df_Southern_2016$ASK[is.na(df_Southern_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southern_2016$OPENPRC, na.rm = TRUE)
df_Southern_2016$OPENPRC[is.na(df_Southern_2016$OPENPRC)] <- OPEN_AVG

df_Southern_2016
```


```{r}
df_Southern_2016$text <- clean_tweets(df_Southern_2016$text)
df_Southern_2016$text <- lemmatize_words(df_Southern_2016$text)
```
```{r}
df_Southern_2018 <- df_Southern_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Southern_2018

#Ticker 
df_Southern_2018$TICKER[is.na(df_Southern_2018$TICKER)] <- "SO"

#PRC
PRC_AVG <- mean(df_Southern_2018$PRC, na.rm = TRUE)
df_Southern_2018$PRC[is.na(df_Southern_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Southern_2018$VOL, na.rm = TRUE)
df_Southern_2018$VOL[is.na(df_Southern_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Southern_2018$BID, na.rm = TRUE)
df_Southern_2018$BID[is.na(df_Southern_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Southern_2018$ASK, na.rm = TRUE)
df_Southern_2018$ASK[is.na(df_Southern_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Southern_2018$OPENPRC, na.rm = TRUE)
df_Southern_2018$OPENPRC[is.na(df_Southern_2018$OPENPRC)] <- OPEN_AVG

df_Southern_2018
```


```{r}
df_Southern_2018$text <- clean_tweets(df_Southern_2018$text)
df_Southern_2018$text <- lemmatize_words(df_Southern_2018$text)
```
```{r}
#Apple
df_Apple_2016 <- df_Apple_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Apple_2016

#Ticker 
df_Apple_2016$TICKER[is.na(df_Apple_2016$TICKER)] <- "AAPL"

#PRC
PRC_AVG <- mean(df_Apple_2016$PRC, na.rm = TRUE)
df_Apple_2016$PRC[is.na(df_Apple_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Apple_2016$VOL, na.rm = TRUE)
df_Apple_2016$VOL[is.na(df_Apple_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Apple_2016$BID, na.rm = TRUE)
df_Apple_2016$BID[is.na(df_Apple_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Apple_2016$ASK, na.rm = TRUE)
df_Apple_2016$ASK[is.na(df_Apple_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Apple_2016$OPENPRC, na.rm = TRUE)
df_Apple_2016$OPENPRC[is.na(df_Apple_2016$OPENPRC)] <- OPEN_AVG

df_Apple_2016
```


```{r}
df_Apple_2016$text <- clean_tweets(df_Apple_2016$text)
df_Apple_2016$text <- lemmatize_words(df_Apple_2016$text)
```


```{r}
```

```{r}
df_Apple_2018 <- df_Apple_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Apple_2018

#Ticker 
df_Apple_2018$TICKER[is.na(df_Apple_2018$TICKER)] <- "AAPL"

#PRC
PRC_AVG <- mean(df_Apple_2018$PRC, na.rm = TRUE)
df_Apple_2018$PRC[is.na(df_Apple_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Apple_2018$VOL, na.rm = TRUE)
df_Apple_2018$VOL[is.na(df_Apple_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Apple_2018$BID, na.rm = TRUE)
df_Apple_2018$BID[is.na(df_Apple_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Apple_2018$ASK, na.rm = TRUE)
df_Apple_2018$ASK[is.na(df_Apple_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Apple_2018$OPENPRC, na.rm = TRUE)
df_Apple_2018$OPENPRC[is.na(df_Apple_2018$OPENPRC)] <- OPEN_AVG

df_Apple_2018
```
```{r}
df_Apple_2018$text <- clean_tweets(df_Apple_2018$text)
df_Apple_2018$text <- lemmatize_words(df_Apple_2018$text)
```

```{r}
#XL
df_XL_2016 <- df_XL_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_XL_2016

#Ticker 
df_XL_2016$TICKER[is.na(df_XL_2016$TICKER)] <- "XL"

#PRC
PRC_AVG <- mean(df_XL_2016$PRC, na.rm = TRUE)
df_XL_2016$PRC[is.na(df_XL_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_XL_2016$VOL, na.rm = TRUE)
df_XL_2016$VOL[is.na(df_XL_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_XL_2016$BID, na.rm = TRUE)
df_XL_2016$BID[is.na(df_XL_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_XL_2016$ASK, na.rm = TRUE)
df_XL_2016$ASK[is.na(df_XL_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_XL_2016$OPENPRC, na.rm = TRUE)
df_XL_2016$OPENPRC[is.na(df_XL_2016$OPENPRC)] <- OPEN_AVG

df_XL_2016
```


```{r}
df_XL_2016$text <- clean_tweets(df_XL_2016$text)
df_XL_2016$text <- lemmatize_words(df_XL_2016$text)
```
```{r}
df_XL_2018 <- df_XL_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_XL_2018

#Ticker 
df_XL_2018$TICKER[is.na(df_XL_2018$TICKER)] <- "XL"

#PRC
PRC_AVG <- mean(df_XL_2018$PRC, na.rm = TRUE)
df_XL_2018$PRC[is.na(df_XL_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_XL_2018$VOL, na.rm = TRUE)
df_XL_2018$VOL[is.na(df_XL_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_XL_2018$BID, na.rm = TRUE)
df_XL_2018$BID[is.na(df_XL_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_XL_2018$ASK, na.rm = TRUE)
df_XL_2018$ASK[is.na(df_XL_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_XL_2018$OPENPRC, na.rm = TRUE)
df_XL_2018$OPENPRC[is.na(df_XL_2018$OPENPRC)] <- OPEN_AVG

df_XL_2018
```


```{r}
df_XL_2018$text <- clean_tweets(df_XL_2018$text)
df_XL_2018$text <- lemmatize_words(df_XL_2018$text)
```
```{r}
#Tyson foods
df_Tysonf_2016 <- df_Tysonf_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Tysonf_2016

#Ticker 
df_Tysonf_2016$TICKER[is.na(df_Tysonf_2016$TICKER)] <- "TSN"

#PRC
PRC_AVG <- mean(df_Tysonf_2016$PRC, na.rm = TRUE)
df_Tysonf_2016$PRC[is.na(df_Tysonf_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Tysonf_2016$VOL, na.rm = TRUE)
df_Tysonf_2016$VOL[is.na(df_Tysonf_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Tysonf_2016$BID, na.rm = TRUE)
df_Tysonf_2016$BID[is.na(df_Tysonf_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Tysonf_2016$ASK, na.rm = TRUE)
df_Tysonf_2016$ASK[is.na(df_Tysonf_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Tysonf_2016$OPENPRC, na.rm = TRUE)
df_Tysonf_2016$OPENPRC[is.na(df_Tysonf_2016$OPENPRC)] <- OPEN_AVG

df_Tysonf_2016
```


```{r}
df_Tysonf_2016$text <- clean_tweets(df_Tysonf_2016$text)
df_Tysonf_2016$text <- lemmatize_words(df_Tysonf_2016$text)
```
```{r}
#Tyson foods
df_Tysonf_2018 <- df_Tysonf_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Tysonf_2018

#Ticker 
df_Tysonf_2018$TICKER[is.na(df_Tysonf_2018$TICKER)] <- "TSN"

#PRC
PRC_AVG <- mean(df_Tysonf_2018$PRC, na.rm = TRUE)
df_Tysonf_2018$PRC[is.na(df_Tysonf_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Tysonf_2018$VOL, na.rm = TRUE)
df_Tysonf_2018$VOL[is.na(df_Tysonf_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Tysonf_2018$BID, na.rm = TRUE)
df_Tysonf_2018$BID[is.na(df_Tysonf_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Tysonf_2018$ASK, na.rm = TRUE)
df_Tysonf_2018$ASK[is.na(df_Tysonf_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Tysonf_2018$OPENPRC, na.rm = TRUE)
df_Tysonf_2018$OPENPRC[is.na(df_Tysonf_2018$OPENPRC)] <- OPEN_AVG

df_Tysonf_2018
```


```{r}
df_Tysonf_2018$text <- clean_tweets(df_Tysonf_2018$text)
df_Tysonf_2018$text <- lemmatize_words(df_Tysonf_2018$text)
```


```{r}
```
```{r}
#Akamai
df_Akamai_2016 <- df_Akamai_2016[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Akamai_2016

#Ticker 
df_Akamai_2016$TICKER[is.na(df_Akamai_2016$TICKER)] <- "AKAM"

#PRC
PRC_AVG <- mean(df_Akamai_2016$PRC, na.rm = TRUE)
df_Akamai_2016$PRC[is.na(df_Akamai_2016$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Akamai_2016$VOL, na.rm = TRUE)
df_Akamai_2016$VOL[is.na(df_Akamai_2016$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Akamai_2016$BID, na.rm = TRUE)
df_Akamai_2016$BID[is.na(df_Akamai_2016$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Akamai_2016$ASK, na.rm = TRUE)
df_Akamai_2016$ASK[is.na(df_Akamai_2016$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Akamai_2016$OPENPRC, na.rm = TRUE)
df_Akamai_2016$OPENPRC[is.na(df_Akamai_2016$OPENPRC)] <- OPEN_AVG

df_Akamai_2016

```
```{r}
df_Akamai_2016$text <- clean_tweets(df_Akamai_2016$text)
df_Akamai_2016$text <- lemmatize_words(df_Akamai_2016$text)
```

```{r}
df_Akamai_2018 <- df_Akamai_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_Akamai_2018

#Ticker 
df_Akamai_2018$TICKER[is.na(df_Akamai_2018$TICKER)] <- "AKAM"

#PRC
PRC_AVG <- mean(df_Akamai_2018$PRC, na.rm = TRUE)
df_Akamai_2018$PRC[is.na(df_Akamai_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_Akamai_2018$VOL, na.rm = TRUE)
df_Akamai_2018$VOL[is.na(df_Akamai_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_Akamai_2018$BID, na.rm = TRUE)
df_Akamai_2018$BID[is.na(df_Akamai_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_Akamai_2018$ASK, na.rm = TRUE)
df_Akamai_2018$ASK[is.na(df_Akamai_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_Akamai_2018$OPENPRC, na.rm = TRUE)
df_Akamai_2018$OPENPRC[is.na(df_Akamai_2018$OPENPRC)] <- OPEN_AVG

df_Akamai_2018
```


```{r}
df_Akamai_2018$text <- clean_tweets(df_Akamai_2018$text)
df_Akamai_2018$text <- lemmatize_words(df_Akamai_2018$text)
```
```{r}
#Google 
df_GOOGL_2015 <-merge(GOOGL_2015, GOOGL_SP_2016, by = "date", all.x = TRUE) 
write.csv(df_GOOGL_2015, file = "Google_2015", row.names = FALSE)

df_GOOGL_2015 <- df_GOOGL_2015[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_GOOGL_2015

#Ticker 
df_GOOGL_2015$TICKER[is.na(df_GOOGL_2015$TICKER)] <- "GOOGL"

#PRC
PRC_AVG <- mean(df_GOOGL_2015$PRC, na.rm = TRUE)
df_GOOGL_2015$PRC[is.na(df_GOOGL_2015$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_GOOGL_2015$VOL, na.rm = TRUE)
df_GOOGL_2015$VOL[is.na(df_GOOGL_2015$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_GOOGL_2015$BID, na.rm = TRUE)
df_GOOGL_2015$BID[is.na(df_GOOGL_2015$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_GOOGL_2015$ASK, na.rm = TRUE)
df_GOOGL_2015$ASK[is.na(df_GOOGL_2015$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GOOGL_2015$OPENPRC, na.rm = TRUE)
df_GOOGL_2015$OPENPRC[is.na(df_GOOGL_2015$OPENPRC)] <- OPEN_AVG

df_GOOGL_2015
```


```{r}
df_GOOGL_2015$text <- clean_tweets(df_GOOGL_2015$text)
df_GOOGL_2015$text <- lemmatize_words(df_GOOGL_2015$text)
```
```{r}
df_GOOGL_2018 <-merge(GOOGL_2018, GOOGLE_SP_2018, by = "date", all.x = TRUE) 
write.csv(df_GOOGL_2018, file = "Google_2018", row.names = FALSE)

df_GOOGL_2018 <- df_GOOGL_2018[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_GOOGL_2018

#Ticker 
df_GOOGL_2018$TICKER[is.na(df_GOOGL_2018$TICKER)] <- "GOOGL"

#PRC
PRC_AVG <- mean(df_GOOGL_2018$PRC, na.rm = TRUE)
df_GOOGL_2018$PRC[is.na(df_GOOGL_2018$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_GOOGL_2018$VOL, na.rm = TRUE)
df_GOOGL_2018$VOL[is.na(df_GOOGL_2018$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_GOOGL_2018$BID, na.rm = TRUE)
df_GOOGL_2018$BID[is.na(df_GOOGL_2018$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_GOOGL_2018$ASK, na.rm = TRUE)
df_GOOGL_2018$ASK[is.na(df_GOOGL_2018$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GOOGL_2018$OPENPRC, na.rm = TRUE)
df_GOOGL_2018$OPENPRC[is.na(df_GOOGL_2018$OPENPRC)] <- OPEN_AVG

df_GOOGL_2018
```
```{r}
df_GOOGL_2018$text <- clean_tweets(df_GOOGL_2018$text)
df_GOOGL_2018$text <- lemmatize_words(df_GOOGL_2018$text)
```


##Covid datasets
```{r}
#WU
df_WU_covid <-merge(WU_covid, WU_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_WU_covid, file = "WU_COVID", row.names = FALSE)

df_WU_covid <- df_WU_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_WU_covid

#Ticker 
df_WU_covid$TICKER[is.na(df_WU_covid$TICKER)] <- "WU"

#PRC
PRC_AVG <- mean(df_WU_covid$PRC, na.rm = TRUE)
df_WU_covid$PRC[is.na(df_WU_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_WU_covid$VOL, na.rm = TRUE)
df_WU_covid$VOL[is.na(df_WU_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_WU_covid$BID, na.rm = TRUE)
df_WU_covid$BID[is.na(df_WU_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_WU_covid$ASK, na.rm = TRUE)
df_WU_covid$ASK[is.na(df_WU_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_WU_covid$OPENPRC, na.rm = TRUE)
df_WU_covid$OPENPRC[is.na(df_WU_covid$OPENPRC)] <- OPEN_AVG

df_WU_covid
```


```{r}
df_WU_covid$text <- clean_tweets(df_WU_covid$text)
df_WU_covid$text <- lemmatize_words(df_WU_covid$text)
```
```{r}
#AAPL
df_AAPL_covid <-merge(AAPL_covid, AAPL_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_AAPL_covid, file = "AAPL_COVID", row.names = FALSE)

df_AAPL_covid <- df_AAPL_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_AAPL_covid

#Ticker 
df_AAPL_covid$TICKER[is.na(df_AAPL_covid$TICKER)] <- "AAPL"

#PRC
PRC_AVG <- mean(df_AAPL_covid$PRC, na.rm = TRUE)
df_AAPL_covid$PRC[is.na(df_AAPL_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_AAPL_covid$VOL, na.rm = TRUE)
df_AAPL_covid$VOL[is.na(df_AAPL_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_AAPL_covid$BID, na.rm = TRUE)
df_AAPL_covid$BID[is.na(df_AAPL_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_AAPL_covid$ASK, na.rm = TRUE)
df_AAPL_covid$ASK[is.na(df_AAPL_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AAPL_covid$OPENPRC, na.rm = TRUE)
df_AAPL_covid$OPENPRC[is.na(df_AAPL_covid$OPENPRC)] <- OPEN_AVG

df_AAPL_covid
```
```{r}
df_AAPL_covid$text <- clean_tweets(df_AAPL_covid$text)
df_AAPL_covid$text <- lemmatize_words(df_AAPL_covid$text)
```

```{r}
#RMD
df_RMD_covid <-merge(RMD_covid, RMD_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_RMD_covid, file = "RMD_COVID", row.names = FALSE)

df_RMD_covid <- df_RMD_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_RMD_covid

#Ticker 
df_RMD_covid$TICKER[is.na(df_RMD_covid$TICKER)] <- "RMD"

#PRC
PRC_AVG <- mean(df_RMD_covid$PRC, na.rm = TRUE)
df_RMD_covid$PRC[is.na(df_RMD_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_RMD_covid$VOL, na.rm = TRUE)
df_RMD_covid$VOL[is.na(df_RMD_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_RMD_covid$BID, na.rm = TRUE)
df_RMD_covid$BID[is.na(df_RMD_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_RMD_covid$ASK, na.rm = TRUE)
df_RMD_covid$ASK[is.na(df_RMD_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RMD_covid$OPENPRC, na.rm = TRUE)
df_RMD_covid$OPENPRC[is.na(df_RMD_covid$OPENPRC)] <- OPEN_AVG

df_RMD_covid
```
```{r}
df_RMD_covid$text <- clean_tweets(df_RMD_covid$text)
df_RMD_covid$text <- lemmatize_words(df_RMD_covid$text)
```

```{r}
#GOOGL
df_GOOGL_covid <-merge(GOOGL_covid, GOOGL_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_GOOGL_covid, file = "GOOGL_COVID", row.names = FALSE)

df_GOOGL_covid <- df_GOOGL_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_GOOGL_covid

#Ticker 
df_GOOGL_covid$TICKER[is.na(df_GOOGL_covid$TICKER)] <- "GOOGL"

#PRC
PRC_AVG <- mean(df_GOOGL_covid$PRC, na.rm = TRUE)
df_GOOGL_covid$PRC[is.na(df_GOOGL_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_GOOGL_covid$VOL, na.rm = TRUE)
df_GOOGL_covid$VOL[is.na(df_GOOGL_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_GOOGL_covid$BID, na.rm = TRUE)
df_GOOGL_covid$BID[is.na(df_GOOGL_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_GOOGL_covid$ASK, na.rm = TRUE)
df_GOOGL_covid$ASK[is.na(df_GOOGL_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_GOOGL_covid$OPENPRC, na.rm = TRUE)
df_GOOGL_covid$OPENPRC[is.na(df_GOOGL_covid$OPENPRC)] <- OPEN_AVG

df_GOOGL_covid
```


```{r}
df_GOOGL_covid$text <- clean_tweets(df_GOOGL_covid$text)
df_GOOGL_covid$text <- lemmatize_words(df_GOOGL_covid$text)
```
```{r}
#AGN
df_AGN_covid <-merge(AGN_covid, AGN_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_AGN_covid, file = "AGN_COVID", row.names = FALSE)

df_AGN_covid <- df_AGN_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_AGN_covid

#Ticker 
df_AGN_covid$TICKER[is.na(df_AGN_covid$TICKER)] <- "AGN"

#PRC
PRC_AVG <- mean(df_AGN_covid$PRC, na.rm = TRUE)
df_AGN_covid$PRC[is.na(df_AGN_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_AGN_covid$VOL, na.rm = TRUE)
df_AGN_covid$VOL[is.na(df_AGN_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_AGN_covid$BID, na.rm = TRUE)
df_AGN_covid$BID[is.na(df_AGN_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_AGN_covid$ASK, na.rm = TRUE)
df_AGN_covid$ASK[is.na(df_AGN_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AGN_covid$OPENPRC, na.rm = TRUE)
df_AGN_covid$OPENPRC[is.na(df_AGN_covid$OPENPRC)] <- OPEN_AVG

df_AGN_covid
```


```{r}
df_AGN_covid$text <- clean_tweets(df_AGN_covid$text)
df_AGN_covid$text <- lemmatize_words(df_AGN_covid$text)
```
```{r}
#AMAZON
df_AMZN_covid <-merge(AMZN_covid, AMZN_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_AMZN_covid, file = "AMZN_COVID", row.names = FALSE)


df_AMZN_covid <- df_AMZN_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_AMZN_covid

#Ticker 
df_AMZN_covid$TICKER[is.na(df_AMZN_covid$TICKER)] <- "AMZN"

#PRC
PRC_AVG <- mean(df_AMZN_covid$PRC, na.rm = TRUE)
df_AMZN_covid$PRC[is.na(df_AMZN_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_AMZN_covid$VOL, na.rm = TRUE)
df_AMZN_covid$VOL[is.na(df_AMZN_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_AMZN_covid$BID, na.rm = TRUE)
df_AMZN_covid$BID[is.na(df_AMZN_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_AMZN_covid$ASK, na.rm = TRUE)
df_AMZN_covid$ASK[is.na(df_AMZN_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_AMZN_covid$OPENPRC, na.rm = TRUE)
df_AMZN_covid$OPENPRC[is.na(df_AMZN_covid$OPENPRC)] <- OPEN_AVG

df_AMZN_covid
```


```{r}

df_AMZN_covid$text <- clean_tweets(df_AMZN_covid$text)
df_AMZN_covid$text <- lemmatize_words(df_AMZN_covid$text)
```
```{r}
#Disney
df_DIS_covid <-merge(DIS_covid, DIS_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_DIS_covid, file = "DIS_COVID", row.names = FALSE)

df_DIS_covid <- df_DIS_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_DIS_covid

#Ticker 
df_DIS_covid$TICKER[is.na(df_DIS_covid$TICKER)] <- "DIS"

#PRC
PRC_AVG <- mean(df_DIS_covid$PRC, na.rm = TRUE)
df_DIS_covid$PRC[is.na(df_DIS_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_DIS_covid$VOL, na.rm = TRUE)
df_DIS_covid$VOL[is.na(df_DIS_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_DIS_covid$BID, na.rm = TRUE)
df_DIS_covid$BID[is.na(df_DIS_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_DIS_covid$ASK, na.rm = TRUE)
df_DIS_covid$ASK[is.na(df_DIS_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_DIS_covid$OPENPRC, na.rm = TRUE)
df_DIS_covid$OPENPRC[is.na(df_DIS_covid$OPENPRC)] <- OPEN_AVG

df_DIS_covid
```


```{r}

df_DIS_covid$text <- clean_tweets(df_DIS_covid$text)
df_DIS_covid$text <- lemmatize_words(df_DIS_covid$text)
```
```{r}
#Microsoft 
df_MSFT_covid <-merge(MSFT_covid, MSFT_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_MSFT_covid, file = "MSFT_COVID", row.names = FALSE)

df_MSFT_covid <- df_MSFT_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_MSFT_covid

#Ticker 
df_MSFT_covid$TICKER[is.na(df_MSFT_covid$TICKER)] <- "MSFT"

#PRC
PRC_AVG <- mean(df_MSFT_covid$PRC, na.rm = TRUE)
df_MSFT_covid$PRC[is.na(df_MSFT_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_MSFT_covid$VOL, na.rm = TRUE)
df_MSFT_covid$VOL[is.na(df_MSFT_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_MSFT_covid$BID, na.rm = TRUE)
df_MSFT_covid$BID[is.na(df_MSFT_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_MSFT_covid$ASK, na.rm = TRUE)
df_MSFT_covid$ASK[is.na(df_MSFT_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_MSFT_covid$OPENPRC, na.rm = TRUE)
df_MSFT_covid$OPENPRC[is.na(df_MSFT_covid$OPENPRC)] <- OPEN_AVG

df_MSFT_covid
```


```{r}
df_MSFT_covid$text <- clean_tweets(df_MSFT_covid$text)
df_MSFT_covid$text <- lemmatize_words(df_MSFT_covid$text)
```
```{r}
#Medtronic
df_MDT_covid <-merge(MDT_Covid, MDT_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_MDT_covid, file = "MDT_COVID", row.names = FALSE)

df_MDT_covid <- df_MDT_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_MDT_covid

#Ticker 
df_MDT_covid$TICKER[is.na(df_MDT_covid$TICKER)] <- "MDT"

#PRC
PRC_AVG <- mean(df_MDT_covid$PRC, na.rm = TRUE)
df_MDT_covid$PRC[is.na(df_MDT_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_MDT_covid$VOL, na.rm = TRUE)
df_MDT_covid$VOL[is.na(df_MDT_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_MDT_covid$BID, na.rm = TRUE)
df_MDT_covid$BID[is.na(df_MDT_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_MDT_covid$ASK, na.rm = TRUE)
df_MDT_covid$ASK[is.na(df_MDT_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_MDT_covid$OPENPRC, na.rm = TRUE)
df_MDT_covid$OPENPRC[is.na(df_MDT_covid$OPENPRC)] <- OPEN_AVG

df_MDT_covid
```


```{r}
df_MDT_covid$text <- clean_tweets(df_MDT_covid$text)
df_MDT_covid$text <- lemmatize_words(df_MDT_covid$text)
```
```{r}
#RedHat
df_RHT_covid <-merge(RHT_covid, RHT_SP_covid, by = "date", all.x = TRUE) 
write.csv(df_RHT_covid, file = "RHT_COVID", row.names = FALSE)

df_RHT_covid <- df_RHT_covid[,-c(6,8,9,10,15,17,18,19,20,21)] 
df_RHT_covid

#Ticker 
df_RHT_covid$TICKER[is.na(df_RHT_covid$TICKER)] <- "RHT"

#PRC
PRC_AVG <- mean(df_RHT_covid$PRC, na.rm = TRUE)
df_RHT_covid$PRC[is.na(df_RHT_covid$PRC)] <- PRC_AVG

#VOL
VOL_AVG <- mean(df_RHT_covid$VOL, na.rm = TRUE)
df_RHT_covid$VOL[is.na(df_RHT_covid$VOL)] <- VOL_AVG

#BID
BID_AVG <- BID_AVG <- mean(df_RHT_covid$BID, na.rm = TRUE)
df_RHT_covid$BID[is.na(df_RHT_covid$BID)] <- BID_AVG

#ASK
ASK_AVG <- ASK_AVG <- mean(df_RHT_covid$ASK, na.rm = TRUE)
df_RHT_covid$ASK[is.na(df_RHT_covid$ASK)] <- ASK_AVG

#OPENPRC
OPEN_AVG <- OPEN_AVG <- mean(df_RHT_covid$OPENPRC, na.rm = TRUE)
df_RHT_covid$OPENPRC[is.na(df_RHT_covid$OPENPRC)] <- OPEN_AVG

df_RHT_covid
```


```{r}
df_RHT_covid$text <- clean_tweets(df_RHT_covid$text)
df_RHT_covid$text <- lemmatize_words(df_RHT_covid$text)
```

##Wordclouds 

```{r}
#Packages 
library(wordcloud)
library(RColorBrewer)
library(wordcloud2)
library(tm)
library("tidyverse")
```
```{r}
#vector containing only the text
text <- df_Test$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Johnson_2016
#vector containing only the text
text <- df_Johnson_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Johnson 2017
#vector containing only the text
text <- df_Johnson_2017$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Nasdaq
#vector containing only the text
text <- df_NASDAQ$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Autodesk 
#vector containing only the text
text <- df_autodesk$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```

```{r}
#Hasbro 
#vector containing only the text
text <- df_Hasbro_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#vector containing only the text
text <- df_Hasbro_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Intel 2016
#vector containing only the text
text <- df_Intel_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Intel 2018
#vector containing only the text
text <- df_Intel_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Activision 2016
#vector containing only the text
text <- df_Activision_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Activision 2016
#vector containing only the text
text <- df_Activision_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Inuit 2016
#vector containing only the text
text <- df_Inuit_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Inuit 2018
#vector containing only the text
text <- df_Inuit_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Allergan 2016
#vector containing only the text
text <- df_Allergan_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Allergan 2018
#vector containing only the text
text <- df_Allergan_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Humana 2016
#vector containing only the text
text <- df_Humana_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Humana 2018
#vector containing only the text
text <- df_Humana_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Autodesk CB 2016
#vector containing only the text
text <- df_CB_autodesk_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Autodesk 2018
#vector containing only the text
text <- df_CB_autodesk_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Equinox 2018
#vector containing only the text
text <- df_equinox_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Cisco 2016
#vector containing only the text
text <- df_cisco_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Cisco 2018
#vector containing only the text
text <- df_cisco_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#EBAY 2016
#vector containing only the text
text <- df_EBAY_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#EBAY 2018
#vector containing only the text
text <- df_EBAY_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```

```{r}
#Davita 2016
#vector containing only the text
text <- df_Davita_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Davita 2018
#vector containing only the text
text <- df_Davita_2017$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Illumina 2016
#vector containing only the text
text <- df_Illumina_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Illumina 2018
#vector containing only the text
text <- df_Illumina_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Homedepot
#vector containing only the text
text <- df_Homedepot_2013$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Southwest airlines 2016
#vector containing only the text
text <- df_Southwest_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Southwest Airlines 2018
#vector containing only the text
text <- df_Southwest_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#FIS 2016
#vector containing only the text
text <- df_FIS_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#FIS 2018
#vector containing only the text
text <- df_FIS_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Leucadia Nation 2016
#vector containing only the text
text <- df_Leucadia_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Leucadia Nation 2018
#vector containing only the text
text <- df_Leucadia_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Verizon
#vector containing only the text
text <- df_Verizon_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Western Union 2016
#vector containing only the text
text <- df_WU_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Western Union 2018
#vector containing only the text
text <- df_WU_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#RedHat 2016
#vector containing only the text
text <- df_RedHat_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#RedHat 2018
#vector containing only the text
text <- df_RedHat_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Amazon 2016
#vector containing only the text
text <- df_AMZN_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Amazon 2018
#vector containing only the text
text <- df_AMZN_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#GE 2016
#vector containing only the text
text <- df_GE_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```

```{r}
#GE 2017
#vector containing only the text
text <- df_GE_2017$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Fiserv 2016
#vector containing only the text
text <- df_Fiserv_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Fiserv 2018
#vector containing only the text
text <- df_Fiserv_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Waste Management 2018
#vector containing only the text
text <- df_WM_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Wills Tower 2016
#vector containing only the text
text <- df_Wills_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Wills Tower 2018
#vector containing only the text
text <- df_Wills_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Tripadvisor 2016
#vector containing only the text
text <- df_tripadvisor_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Tripadvisor 2018

#vector containing only the text
text <- df_tripadvisor_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#DavitaKent 

#vector containing only the text
text <- df_DavitaK_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Davita Kent 2018
#vector containing only the text
text <- df_DavitaK_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Starbucks 2018
#vector containing only the text
text <- df_Starbucks_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#McCormick 2018
#vector containing only the text
text <- df_McCormick_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```

```{r}
#IHS Markit 2018
#vector containing only the text
text <- df_IHS_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#AMD 2018
#vector containing only the text
text <- df_AMD_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#ResMed 2016
#vector containing only the text
text <- df_ResMed_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#ResMed 2018
#vector containing only the text
text <- df_ResMed_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#CA 2016
#vector containing only the text
text <- df_CA_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#CA 2018
#vector containing only the text
text <- df_CA_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#GM 2016
#vector containing only the text
text <- df_GM_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#GM 2018
#vector containing only the text
text <- df_GM_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```

```{r}
#Aetna 2016
#vector containing only the text
text <- df_Aetna_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Aetna 2018
#vector containing only the text
text <- df_Aetna_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#NRG 2016
#vector containing only the text
text <- df_NRG_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#NRG 2018
#vector containing only the text
text <- df_NRG_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}
#Medtronic 2016
#vector containing only the text
text <- df_Medtronic_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```

```{r}
#Medtronic 2018
#vector containing only the text
text <- df_Medtronic_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Juniper 2016
#vector containing only the text
text <- df_Juniper_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```

```{r}
#Juniper 2018
#vector containing only the text
text <- df_Juniper_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```
```{r}

#Aetna Ramon
#vector containing only the text
text <- df_AetnaR_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Netflix
#vector containing only the text
text <- df_Netflix_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Disney
#vector containing only the text
text <- df_Disney_2018$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))

```
```{r}
#Fox 2015
#vector containing only the text
text <- df_Fox_2015$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}
#Microsoft 2016
#vector containing only the text
text <- df_Microsoft_2016$text
#corpus  
docs <- Corpus(VectorSource(text))

docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df_wc <- data.frame(word = names(words),freq=words)

wordcloud(words = df_wc$word, freq = df_wc$freq, min.freq = 1,max.words=200, random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2"))
```

```{r}

```

